Methods and compositions for diagnosis of thyroid conditions

ABSTRACT

The present invention relates to compositions, kits, and methods for molecular profiling and cancer diagnostics, including but not limited to genomic DNA markers associated with cancer. In particular, the present invention provides molecular profiles associated with thyroid cancer, methods of determining molecular profiles, and methods of analyzing results to provide a diagnosis.

CROSS-REFERENCE

This application is a Continuation application which claims the benefit of U.S. application Ser. No. 13/318,751, filed May 10, 2012; which is a national stage application of PCT/US2010/34140, filed May 7, 2010, which claims the benefit of U.S. Provisional Application No. 61/176,471, filed on May 7, 2009, entitled “Methods and Compositions for Diagnosis of Thyroid Conditions,” which application is incorporated herein by reference in its entirety.

BACKGROUND OF THE INVENTION

Cancer is the second leading cause of death in the United States and one of the leading causes of mortality worldwide. Nearly 25 million people are currently living with cancer, with 11 million new cases diagnosed each year. Furthermore, as the general population continues to age, cancer will become a bigger and bigger problem. The World Health Organization projects that by the year 2020, global cancer rates will increase by 50%.

The thyroid is a gland in the neck that has at least two kinds of cells that make hormones. Follicular cells make thyroid hormone, which affects heart rate, body temperature, and energy level. C cells make calcitonin, a hormone that helps control the level of calcium in the blood. Abnormal growth in the thyroid can results in the formation of nodules, which can be either benign or malignant. Thyroid cancer includes at least four different kinds of malignant tumors of the thyroid gland: papillary, follicular, medullary and anaplastic.

It is estimated that out of the approximately 120,000 thyroid removal surgeries performed each year due to suspected malignancy in the United States, only about 33,000 are necessary. Thus, approximately 90,000 unnecessary surgeries are performed. In addition, there are continued treatment costs and complications due to the need for lifelong drug therapy to replace the lost thyroid function. Accordingly, there is a need for improved testing procedures that improve upon current methods of cancer diagnosis.

SUMMARY OF THE INVENTION

The present invention includes a method for diagnosing thyroid disease in a subject, the method comprising (a) providing a DNA sample from a subject; (b) detecting the presence of one or more polymorphisms selected from the group consisting of the polymorphisms listed in Tables 1, 3-6, 8 or lists 1-45 or their complement; and (c) determining whether said subject has or is likely to have a malignant or benign thyroid condition based on the results of step (b).

The present invention also includes a composition comprising one or more binding agents that specifically bind to the one or more polymorphisms selected from the group consisting of the polymorphisms listed in Tables 1, 3-6, 8 or lists 1-45 or their complement.

In another embodiment, the present invention includes a kit for diagnosing thyroid disease in a subject, the kit comprising: (a) at least one binding agent that specifically binds to the one or more polymorphisms selected from the group consisting of the polymorphisms listed in Tables 1, 3-6, 8 or lists 1-45 or their complement; and (b) reagents for detecting binding of said at least one binding agent to a DNA sample from a subject.

In another embodiment, the present invention includes a business method for diagnosing thyroid disease in a subject, the business method comprising: (a) diagnosing thyroid disease from a subject using the method stated above; (b) providing the results of the diagnosis to the subject, a healthcare provider, or a third party; and (c) billing said subject, healthcare provider, or third party.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of the invention are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention are utilized, and the accompanying drawings of which:

FIG. 1 depicts a data analysis flow chart for identifying genomic regions that distinguish between benign and malignant thyroid tissue samples. HAPMAP, haplotype map version 3—a set of reference files publicly available at www.HAPMAP.org; CBS, circular binary segmentation-a data analysis algorithm (Olshen, Venkatraman et al. 2004); PLINK-a free, open-source whole genome association analysis toolset available at: http://pngu.mgh.harvard.edu/˜purcell/plink/index.shtml (Purcell, Neale et al. 2007).

FIG. 2: FIG. 2A is a flow chart describing how molecular profiling may be used to improve the accuracy of routine cytological examination. FIG. 2B describes alternate embodiments of the molecular profiling business.

FIG. 3 is an illustration of a kit provided by the molecular profiling business.

FIG. 4 is an illustration of a molecular profiling results report.

FIG. 5 depicts a computer useful for displaying, storing, retrieving, or calculating diagnostic results from the molecular profiling; displaying, storing, retrieving, or calculating raw data from genomic or nucleic acid expression analysis; or displaying, storing, retrieving, or calculating any sample or customer information useful in the methods of the present invention.

FIG. 6 is a Venn diagram analysis across copy number datasets showing the level of overlap between gene lists. FIG. 6A, the lists of genes mapped to the genomic regions described in tables 3, 4 and 5 were cross-checked to determine their level of redundancy. Only 2 genes overlapped between the FVPTC vs. NHP and the PTC vs. NHP gene lists (Tables 4 and 5). FIG. 6B, a combined list of 117 unique genes from panel A was compared to the list of 199 genes from the Malignant vs. Benign analysis (table 6), showing an overlap of 24 genes between them. FIG. 6C, a comparison of the 76 genes listed in FC vs. FA analyses compared against the 199 genes listed in the Malignant vs. Benign analyses (Tables 3 and 6). FIG. 6D, a comparison of the gene lists from the FVPTC vs. NHP, Malignant vs. Benign, and PTC vs. NHP analyses (tables 4, 5, and 6) showing the level of overlap between each list.

FIG. 7. A comparison of 292 genes that distinguish thyroid nodules that were discovered using DNA copy number analysis compared against 4918 genes previously discovered by mRNA expression and alternative exon usage analysis (see U.S. patent application Ser. No. 12/592,065, which is hereby incorporated by reference in its entirety), showing the level of overlap between the lists.

DETAILED DESCRIPTION OF THE INVENTION

The present disclosure provides novel methods for diagnosing abnormal cellular proliferation from a biological test sample, and related kits and compositions. The present invention also provides methods and compositions for differential diagnosis of types of aberrant cellular proliferation such as carcinomas including follicular carcinomas (FC), follicular variant of papillary thyroid carcinomas (FVPTC), Hurthle cell carcinomas (HC), Hurthle cell adenomas (HA); papillary thyroid carcinomas (PTC), medullary thyroid carcinomas (MTC), and anaplastic carcinomas (ATC); adenomas including follicular adenomas (FA); nodule hyperplasias (NHP); colloid nodules (CN); benign nodules (BN); follicular neoplasms (FN); lymphocytic thyroiditis (LCT), including lymphocytic autoimmune thyroiditis; parathyroid tissue; renal carcinoma metastasis to the thyroid; melanoma metastasis to the thyroid; B-cell lymphoma metastasis to the thyroid; breast carcinoma to the thyroid; benign (B) tumors, malignant (M) tumors, and normal (N) tissues.

The methods and compositions of the present invention identify human genomic regions which are useful for detecting, diagnosing and prognosing thyroid cancer. In some embodiments, the genomic regions can distinguish malignant thyroid nodules from benign. In other embodiments, the genomic regions can distinguish particular malignant thyroid nodules as being papillary, follicular, medullary or anaplastic. The genomic regions are associated with chromosomal abnormalities and copy number changes in human thyroid neoplasms, including benign and malignant neoplasms. These sequences are used as probes and in methods to detect copy number changes to screen for the presence of disease, and in the prognosis for aggressive tumor behavior and response to therapy. Additionally the present invention provides business methods for providing enhanced diagnosis, differential diagnosis, monitoring, and treatment of cellular proliferation.

Typically, screening for the presence of a tumor or other type of cancer, involves analyzing a biological sample taken by various methods such as, for example, a biopsy. The biological sample is then prepared and examined by one skilled in the art. The methods of preparation can include but are not limited to various cytological stains, and immuno-histochemical methods. Unfortunately, traditional methods of cancer diagnosis suffer from a number of deficiencies. These deficiencies include: 1) the diagnosis may require a subjective assessment and thus be prone to inaccuracy and lack of reproducibility, 2) the methods may fail to determine the underlying genetic, metabolic or signaling pathways responsible for the resulting pathogenesis, 3) the methods may not provide a quantitative assessment of the test results, and 4) the methods may be unable to provide an unambiguous diagnosis for certain samples.

Identification of Markers for Use in Detection of Copy Number Variants

In one embodiment of the invention, markers and genes can be identified to have a differential copy number in thyroid cancer samples compared to thyroid benign samples. Illustrative samples having a benign pathology include follicular adenoma, Hurtle cell adenoma, lymphocytic thyroiditis, and nodular hyperplasia. Illustrative samples of malignant pathology include follicular carcinoma, follicular variant of papillary thyroid carcinoma, and papillary thyroid carcinoma. In one embodiment, the methods of the present invention seek to improve upon the accuracy of current methods of cancer diagnosis. Improved accuracy can result from the measurement of multiple genes and/or expression markers, the identification of gene expression products such as miRNAs, rRNA, tRNA and mRNA gene expression products with high diagnostic power or statistical significance, or the identification of groups of genes and/or expression products with high diagnostic power or statistical significance, or any combination thereof.

Gene copy number within a defined group, such as receptor tyrosine kinases, may be indicative of a disease or condition when copy number levels are higher or lower than normal. The measurement of copy number of other genes within that same group can provide diagnostic utility. Thus, in one embodiment, the invention measures two or more gene copy numbers that are within a group. For example, in some embodiments, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45 or 50 gene copy numbers are measured from a group. Various groups are defined within the specification, such as groups useful for diagnosis of subtypes of thyroid cancer or groups that fall within particular ontology groups. In another embodiment, it would be advantageous to measure sets of genes that accurately indicate the presence or absence of cancer from multiple groups. For example, the invention contemplates the use of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45 or 50 groups, each with 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45 or 50 gene copy numbers measured.

Additionally, increased copy number of other oncogenes such as for example Ras in a biological sample may also be indicative of the presence of cancerous cells. In some cases, it may be advantageous to determine the copy number of several different classes of oncogenes such as for example receptor tyrosine kinases, cytoplasmic tyrosine kinases, GTPases, serine/threonine kinases, lipid kinases, mitogens, growth factors, and transcription factors. The determination of copy number of genes of different classes or groups involved in cancer progression may in some cases increase the diagnostic power of the present invention.

Groups of expression markers may include markers within a metabolic or signaling pathway, or genetically or functionally homologous markers. For example, one group of markers may include genes involved in the epithelial growth factor signaling pathway. Another group of markers may include mitogen-activated protein kinases. The present invention also provides methods and compositions for detecting (i.e. measuring) measuring gene copy numbers from multiple and/or independent metabolic or signaling pathways.

In one embodiment, gene copy numbers of the present invention may provide increased accuracy of cancer diagnosis through the use of multiple gene copy number analyses and statistical analysis. In particular, the present invention provides, but is not limited to, DNA copy number profiles associated with thyroid cancers. The present invention also provides methods of characterizing thyroid tissue samples, and kits and compositions useful for the application of said methods. The disclosure further includes methods for running a molecular profiling business.

The present disclosure provides methods and compositions for improving upon the current state of the art for diagnosing cancer.

In some embodiments, the present invention provides a method of diagnosing cancer that gives a specificity or sensitivity that is greater than 70% using the subject methods described herein, wherein the gene copy number levels are compared between the biological sample and a control sample; and identifying the biological sample as cancerous if there is a difference in the gene copy number levels between the biological sample and the control sample at a specified confidence level. In some embodiments, the specificity and/or sensitivity of the present method is at least 70%, 75%, 80%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or more.

In some embodiments, the nominal specificity is greater than or equal to 70%. The nominal negative predictive value (NPV) is greater than or equal to 95%. In some embodiments, the NPV is at least 95%, 95.5%, 96%, 96.5%, 97%, 97.5%, 98%, 98.5%, 99%, 99.5% or more.

Sensitivity typically refers to TP/(TP+FN), where TP is true positive and FN is false negative. Number of Continued Indeterminate results divided by the total number of malignant results based on adjudicated histopathology diagnosis. Specificity typically refers to TN/(TN+FP), where TN is true negative and FP is false positive. The number of benign results divided by the total number of benign results based on adjudicated histopathology diagnosis. Positive Predictive Value (PPV): TP/(TP+FP); Negative Predictive Value (NPV): TN/(TN+FN).

In some embodiments, the difference in gene copy number level is at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more copies. In some embodiments, the difference in gene copy number level is at least 2, 3, 4, 5, 6, 7, 8, 9, 10 fold or more. In some embodiments, the biological sample is identified as cancerous with an accuracy of greater than 75%, 80%, 85%, 90%, 95%, 99% or more. In some embodiments, the biological sample is identified as cancerous with a sensitivity of greater than 95%. In some embodiments, the biological sample is identified as cancerous with a specificity of greater than 95%. In some embodiments, the biological sample is identified as cancerous with a sensitivity of greater than 95% and a specificity of greater than 95%. In some embodiments, the accuracy is calculated using a trained algorithm.

Software can be used to extract, normalize, and summarize array intensity data from markers across the human genome. In some embodiments, the relative intensity of a given probe in either the benign or malignant sample can be compared against a reference set to determine whether a copy number aberration is present. An increase in relative intensity is indicative of copy number gain (amplification), and a decrease in relative intensity is indicative of copy number loss (deletion).

The resulting relative intensities for each sample can be translated into segments (regions of equal copy number data), for example, using circular binary segmentation (CBS) (Olshen, Venkatraman et al. 2004). These segments can then used to create non-overlapping features among samples, for example, using PLINK-a free whole genome association analysis toolset (Purcell, Neale et al. 2007). The top features associated with a malignant or benign disease label can be identified using statistical means such as chi-square tests and PLINK. Classification can be performed, for example, by using top PLINK features and support vector machine (SVM) analysis.

Diagnostic Markers

In one embodiment of the invention, genomic regions that distinguish follicular carcinoma from follicular adenoma are shown in Table 3.

In another embodiment of the invention, genomic regions that distinguish the follicular variants of papillary carcinoma from nodular hyperplasia are shown in Table 4.

In another embodiment of the invention, genomic regions that distinguish the papillary thyroid carcinoma from nodular hyperplasia are shown in Table 5.

Use of Markers and Genes for Detecting Thyroid Conditions

The markers and genes of the present invention can be utilized to characterize the cancerous or non-cancerous status of cells, or tissues.

The present invention includes a method for diagnosing thyroid cancer in a subject, comprising determining amplification of a marker or gene in a thyroid sample of a subject wherein said marker or gene is a marker or gene of Table 1, 3, 4, 5, 6, or 8, or lists 1-45. Genes were mapped to microarray sequences using Affymetrix annotation file GenomeWideSNP_(—)6.na26, based on Human Genome Build 18.

In one embodiment, the present invention includes a method for diagnosing follicular carcinoma from follicular adenoma, comprising determining amplification of a marker or gene in a thyroid sample of a subject wherein said marker or gene is a marker or gene of Table 3.

In one embodiment, the present invention includes a method for diagnosing follicular variants of papillary carcinoma from nodular hyperplasia, comprising determining amplification of a marker or gene in a thyroid sample of a subject wherein said marker or gene is a marker or gene of Table 4.

In one embodiment, the present invention includes a method for diagnosing papillary thyroid carcinoma from nodular hyperplasia, comprising determining amplification of a marker or gene in a thyroid sample of a subject wherein said marker or gene is a marker or gene of Table 5.

In methods of the present invention, one or more markers or genes can be used to detect a thyroid condition. A combination of markers or genes can be used to increase the sensitivity and/or specificity of detection of thyroid cancer and/or detect subtypes of thyroid cancer.

The present invention also includes a method for detecting the cancerous status of a cell, comprising detecting expression in a cell of at least one disclosed in Tables 1, 3, 4, 5, 6 or 8, or lists 1-45. In one embodiment, elevated expression may be monitored by comparison to expression in normal cells having the same genes. Elevated expression of these genes is indicative of the cancerous state.

Increased expression, such as increased copy number, may be determined for a sample suspected of having thyroid cancer by using the nucleotide sequences as identified in Table 1, 3, 4, 5, 6 or 8 or lists 1-45 as a means of generating probes for the DNAs present in the cells to be examined. Thus, the DNA of such cells may be extracted and probed using the sequences disclosed herein for the presence in the genomes of such cells of increased amounts of one or more of the genes of the invention. For example, where a cancer-related, or cancer-linked, gene as disclosed herein is found to be present in multiple copies within the genome of a cell, even where it may not be actively being over-expressed at the time of such determination, this may be indicative of at least a disposition toward developing cancer at a subsequent time.

In some embodiments, the present invention provides a method of diagnosing cancer comprising using gene copy number analysis from one or more of the following signaling pathways. The signaling pathways from which the genes can be selected include but are not limited to: acute myeloid leukemia signaling, somatostatin receptor 2 signaling, cAMP-mediated signaling, cell cycle and DNA damage checkpoint signaling, G-protein coupled receptor signaling, integrin signaling, melanoma cell signaling, relaxin signaling, and thyroid cancer signaling. In some embodiments, more than one gene is selected from a single signaling pathway to determine and compare the differential gene copy number level between the biological sample and a control sample. Other signaling pathways include, but are not limited to, an adherens, ECM, thyroid cancer, focal adhesion, apoptosis, p53, tight junction, TGFbeta, ErbB, Wnt, pathways in cancer overview, cell cycle, VEGF, Jak/STAT, MAPK, PPAR, mTOR or autoimmune thyroid pathway. In other embodiments, at least two genes are selected from at least two different signaling pathways to determine and compare the differential gene copy number level between the biological sample and the control sample. Methods and compositions of the invention can have genes selected from 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50 or more signaling pathways and can have from 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50 or more genes from each signaling pathway, in any combination. In some embodiments, the set of genes combined give a specificity or sensitivity of greater than 70%, 75%, 80%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 99.5%, or a positive predictive value or negative predictive value of at least 95%, 95.5%, 96%, 96.5%, 97%, 97.5%, 98%, 98.5%, 99%, 99.5% or more.

In some embodiments, the present invention provides a method of diagnosing cancer comprising genes selected from at least two different ontology groups. In some embodiments, the ontology groups from which the genes can be selected include but are not limited to: multicellular organismal process, multicellular organismal development, anterior-posterior pattern formation, epidermis development, regionalization, ectoderm development, keratinization, developmental process, tissue development, regulation of cellular process, system development, regulation of biological process, anatomical structure development, biological regulation, pattern specification process, keratinocyte differention, epidermal cell differentiation, organ development, sequence-specific DNA binding, regulation of transcription DNA-dependent, embryonic development, regulation of RNA metabolic process, ARF guanyl-nucleotide exchange factor activity, transcription DNA-dependent, RNA biosynthetic process, cell surface receptor linked signal transduction, signal transducer activity, N-methyl-D-aspartate selective glutamate receptor complex, molecular transducer activity, or heart trabecula formation. In some embodiments, more than one gene is selected from a single ontology group to determine and compare the differential gene copy number level between the biological sample and a control sample. In other embodiments, at least two genes are selected from at least two different ontology groups to determine and compare the differential gene copy number level between the biological sample and the control sample. Methods and compositions of the invention can have genes selected from 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50 or more gene ontology groups and can have from 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50 or more genes from each gene ontology group, in any combination. In some embodiments, the set of genes combined give a specificity or sensitivity of greater than 70%, 75%, 80%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 99.5%, or a positive predictive value or negative predictive value of at least 95%, 95.5%, 96%, 96.5%, 97%, 97.5%, 98%, 98.5%, 99%, 99.5% or more.

In some embodiments, the biological sample is classified as cancerous or positive for a subtype of cancer with an accuracy of greater than 75%, 80%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 99.5%. The diagnosis accuracy as used herein includes specificity, sensitivity, positive predictive value, negative predictive value, and/or false discovery rate.

When classifying a biological sample for diagnosis of cancer, there are typically four possible outcomes from a binary classifier. If the outcome from a prediction is p and the actual value is also p, then it is called a true positive (TP); however if the actual value is n then it is said to be afalse positive (FP). Conversely, a true negative has occurred when both the prediction outcome and the actual value are n, and false negative is when the prediction outcome is n while the actual value is p. In one embodiment, consider a diagnostic test that seeks to determine whether a person has a certain disease. A false positive in this case occurs when the person tests positive, but actually does not have the disease. A false negative, on the other hand, occurs when the person tests negative, suggesting they are healthy, when they actually do have the disease. In some embodiments, ROC curve assuming real-world prevalence of subtypes can be generated by re-sampling errors achieved on available samples in relevant proportions.

The positive predictive value (PPV), or precision rate, or post-test probability of disease, is the proportion of patients with positive test results who are correctly diagnosed. It is the most important measure of a diagnostic method as it reflects the probability that a positive test reflects the underlying condition being tested for. Its value does however depend on the prevalence of the disease, which may vary. In one example, FP (false positive); TN (true negative); TP (true positive); FN (false negative).

False positive rate (α)=FP/(FP+TN)−specificity

False negative rate (β)=FN/(TP+FN)−sensitivity

Power=sensitivity=1−β

Likelihood-ratio positive=sensitivity/(1−specificity)

Likelihood-ratio negative=(1−sensitivity)/specificity

The negative predictive value is the proportion of patients with negative test results who are correctly diagnosed. PPV and NPV measurements can be derived using appropriate disease subtype prevalence estimates. An estimate of the pooled malignant disease prevalence can be calculated from the pool of indeterminates which roughly classify into B vs M by surgery. For subtype specific estimates, in some embodiments, disease prevalence may sometimes be incalculable because there are not any available samples. In these cases, the subtype disease prevalence can be substituted by the pooled disease prevalence estimate.

In some embodiments, the results of the genetic analysis of the subject methods provide a statistical confidence level that a given diagnosis is correct. In some embodiments, such statistical confidence level is above 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 99.5%.

In one aspect of the present disclosure, samples that have been processed by a cytological company, subjected to routine methods and stains, diagnosed and categorized, are then subjected to molecular profiling as a second diagnostic screen. This second diagnostic screen enables: 1) a significant reduction of false positives and false negatives, 2) a determination of the underlying genetic, metabolic, or signaling pathways responsible for the resulting pathology, 3) the ability to assign a statistical probability to the accuracy of the diagnosis, 4) the ability to resolve ambiguous results, and 5) the ability to distinguish between sub-types of cancer.

For example, in the specific case of thyroid cancer, molecular profiling of the present invention may further provide a diagnosis for the specific type of thyroid cancer (e.g. papillary, follicular, medullary, or anaplastic). The results of the molecular profiling may further allow one skilled in the art, such as a scientist or medical professional to suggest or prescribe a specific therapeutic intervention. Molecular profiling of biological samples may also be used to monitor the efficacy of a particular treatment after the initial diagnosis. It is further understood that in some cases, molecular profiling may be used in place of, rather than in addition to, established methods of cancer diagnosis.

In one aspect, the present invention provides algorithms and methods that can be used for diagnosis and monitoring of a genetic disorder. A genetic disorder is an illness caused by abnormalities in genes or chromosomes. While some diseases, such as cancer, are due in part to genetic disorders, they can also be caused by environmental factors. In some embodiments, the algorithms and the methods disclosed herein are used for diagnosis and monitoring of a cancer such as thyroid cancer.

Genetic disorders can be typically grouped into two categories: single gene disorders and multifactorial and polygenic (complex) disorders. A single gene disorder is the result of a single mutated gene. There are estimated to be over 4000 human diseases caused by single gene defects. Single gene disorders can be passed on to subsequent generations in several ways. There are several types of inheriting a single gene disorder including but not limited to autosomal dominant, autosomal recessive, X-linked dominant, X-linked recessive, Y-linked and mitochondrial inheritance. Only one mutated copy of the gene will be necessary for a person to be affected by an autosomal dominant disorder. Examples of autosomal dominant type of disorder include but are not limited to Huntington's disease, Neurofibromatosis 1, Marfan Syndrome, Hereditary nonpolyposis colorectal cancer, and Hereditary multiple exostoses. In autosomal recessive disorder, two copies of the gene must be mutated for a person to be affected by an autosomal recessive disorder. Examples of this type of disorder include but are not limited to cystic fibrosis, sickle-cell disease (also partial sickle-cell disease), Tay-Sachs disease, Niemann-Pick disease, spinal muscular atrophy, and dry earwax. X-linked dominant disorders are caused by mutations in genes on the X chromosome. Only a few disorders have this inheritance pattern, with a prime example being X-linked hypophosphatemic rickets. Males and females are both affected in these disorders, with males typically being more severely affected than females. Some X-linked dominant conditions such as Rett syndrome, Incontinentia Pigmenti type 2 and Aicardi Syndrome are usually fatal in males either in utero or shortly after birth, and are therefore predominantly seen in females. X-linked recessive disorders are also caused by mutations in genes on the X chromosome. Examples of this type of disorder include but are not limited to Hemophilia A, Duchenne muscular dystrophy, red-green color blindness, muscular dystrophy and Androgenetic alopecia. Y-linked disorders are caused by mutations on the Y chromosome. Examples include but are not limited to Male Infertility and hypertrichosis pinnae. Mitochondrial inheritance, also known as maternal inheritance, applies to genes in mitochondrial DNA. An example of this type of disorder is Leber's Hereditary Optic Neuropathy.

Genetic disorders may also be complex, multifactorial or polygenic, this means that they are likely associated with the effects of multiple genes in combination with lifestyle and environmental factors. Although complex disorders often cluster in families, they do not have a clear-cut pattern of inheritance. This makes it difficult to determine a person's risk of inheriting or passing on these disorders. Complex disorders are also difficult to study and treat because the specific factors that cause most of these disorders have not yet been identified. Multifactoral or polygenic disorders that can be diagnosed, characterized and/or monitored using the algorithms and methods of the present invention include but are not limited to heart disease, diabetes, asthma, autism, autoimmune diseases such as multiple sclerosis, cancers, ciliopathies, cleft palate, hypertension, inflammatory bowel disease, mental retardation and obesity.

Other genetic disorders that can be diagnosed, characterized and/or monitored using the algorithms and methods of the present invention include but are not limited to 1p36 deletion syndrome, 21-hydroxylase deficiency, 22q11.2 deletion syndrome, 47, XYY syndrome, 48, XXXX, 49, XXXXX, aceruloplasminemia, achondrogenesis, type II, achondroplasia, acute intermittent porphyria, adenylosuccinate lyase deficiency, Adrenoleukodystrophy, ALA deficiency porphyria, ALA dehydratase deficiency, Alexander disease, alkaptonuria, alpha-1 antitrypsin deficiency, Alstrom syndrome, Alzheimer's disease (type 1, 2, 3, and 4), Amelogenesis Imperfecta, amyotrophic lateral sclerosis, Amyotrophic lateral sclerosis type 2, Amyotrophic lateral sclerosis type 4, amyotrophic lateral sclerosis type 4, androgen insensitivity syndrome, Anemia, Angelman syndrome, Apert syndrome, ataxia-telangiectasia, Beare-Stevenson cutis gyrata syndrome, Benjamin syndrome, beta thalassemia, biotinidase deficiency, Hirt-Hogg-Dube syndrome, bladder cancer, Bloom syndrome, Bone diseases, breast cancer, CADASIL, Camptomelic dysplasia, Canavan disease, Cancer, Celiac Disease, CGD Chronic Granulomatous Disorder, Charcot-Marie-Tooth disease, Charcot-Marie-Tooth disease Type 1, Charcot-Marie-Tooth disease Type 4, Charcot-Marie-Tooth disease, type 2, Charcot-Marie-Tooth disease, type 4, Cockayne syndrome, Coffin-Lowry syndrome, collagenopathy, types II and XI, Colorectal Cancer, Congenital absence of the vas deferens, congenital bilateral absence of vas deferens, congenital diabetes, congenital erythropoietic porphyria, Congenital heart disease, congenital hypothyroidism, Connective tissue disease, Cowden syndrome, Cri du chat, Crohn's disease, fibrostenosing, Crouzon syndrome, Crouzonodermoskeletal syndrome, cystic fibrosis, De Grouchy Syndrome, Degenerative nerve diseases, Dent's disease, developmental disabilities, DiGeorge syndrome, Distal spinal muscular atrophy type V, Down syndrome, Dwarfism, Ehlers-Danlos syndrome, Ehlers-Danlos syndrome arthrochalasia type, Ehlers-Danlos syndrome classical type, Ehlers-Danlos syndrome dermatosparaxis type, Ehlers-Danlos syndrome kyphoscoliosis type, vascular type, erythropoietic protoporphyria, Fabry's disease, Facial injuries and disorders, factor V Leiden thrombophilia, familial adenomatous polyposis, familial dysautonomia, fanconi anemia, FG syndrome, fragile X syndrome, Friedreich ataxia, Friedreich's ataxia, G6PD deficiency, galactosemia, Gaucher's disease (type 1, 2, and 3), Genetic brain disorders, Glycine encephalopathy, Haemochromatosis type 2, Haemochromatosis type 4, Harlequin Ichthyosis, Head and brain malformations, Hearing disorders and deafness, Hearing problems in children, hemochromatosis (neonatal, type 2 and type 3), hemophilia, hepatoerythropoietic porphyria, hereditary coproporphyria, Hereditary Multiple Exostoses, hereditary neuropathy with liability to pressure palsies, hereditary nonpolyposis colorectal cancer, homocystinuria, Huntington's disease, Hutchinson Gilford Progeria Syndrome, hyperoxaluria, primary, hyperphenylalaninemia, hypochondrogenesis, hypochondroplasia, idicl5, incontinentia pigmenti, Infantile Gaucher disease, infantile-onset ascending hereditary spastic paralysis, Infertility, Jackson-Weiss syndrome, Joubert syndrome, Juvenile Primary Lateral Sclerosis, Kennedy disease, Klinefelter syndrome, Kniest dysplasia, Krabbe disease, Learning disability, Lesch-Nyhan syndrome, Leukodystrophies, Li-Fraumeni syndrome, lipoprotein lipase deficiency, familial, Male genital disorders, Marfan syndrome, McCune-Albright syndrome, McLeod syndrome, Mediterranean fever, familial, MEDNIK, Menkes disease, Menkes syndrome, Metabolic disorders, methemoglobinemia beta-globin type, Methemoglobinemia congenital methaemoglobinaemia, methylmalonic acidemia, Micro syndrome, Microcephaly, Movement disorders, Mowat-Wilson syndrome, Mucopolysaccharidosis (MPS I), Muenke syndrome, Muscular dystrophy, Muscular dystrophy, Duchenne and Becker type, muscular dystrophy, Duchenne and Becker types, myotonic dystrophy, Myotonic dystrophy type 1 and type 2, Neonatal hemochromatosis, neurofibromatosis, neurofibromatosis 1, neurofibromatosis 2, Neurofibromatosis type I, neurofibromatosis type II, Neurologic diseases, Neuromuscular disorders, Niemann-Pick disease, Nonketotic hyperglycinemia, nonsyndromic deafness, Nonsyndromic deafness autosomal recessive, Noonan syndrome, osteogenesis imperfecta (type I and type III), otospondylomegaepiphyseal dysplasia, pantothenate kinase-associated neurodegeneration, Patau Syndrome (Trisomy 13), Pendred syndrome, Peutz-Jeghers syndrome, Pfeiffer syndrome, phenylketonuria, porphyria, porphyria cutanea tarda, Prader-Willi syndrome, primary pulmonary hypertension, prion disease, Progeria, propionic acidemia, protein C deficiency, protein S deficiency, pseudo-Gaucher disease, pseudoxanthoma elasticum, Retinal disorders, retinoblastoma, retinoblastoma FA—Friedreich ataxia, Rett syndrome, Rubinstein-Taybi syndrome, SADDAN, Sandhoff disease, sensory and autonomic neuropathy type III, sickle cell anemia, skeletal muscle regeneration, Skin pigmentation disorders, Smith Lemli Opitz Syndrome, Speech and communication disorders, spinal muscular atrophy, spinal-bulbar muscular atrophy, spinocerebellar ataxia, spondyloepimetaphyseal dysplasia, Strudwick type, spondyloepiphyseal dysplasia congenita, Stickler syndrome, Stickler syndrome COL2A1, Tay-Sachs disease, tetrahydrobiopterin deficiency, thanatophoric dysplasia, thiamine-responsive megaloblastic anemia with diabetes mellitus and sensorineural deafness, Thyroid disease, Tourette's Syndrome, Treacher Collins syndrome, triple X syndrome, tuberous sclerosis, Turner syndrome, Usher syndrome, variegate porphyria, von Hippel-Lindau disease, Waardenburg syndrome, Weissenbacher-Zweymüller syndrome, Wilson disease, Wolf-Hirschhorn syndrome, Xeroderma Pigmentosum, X-linked severe combined immunodeficiency, X-linked sideroblastic anemia, and X-linked spinal-bulbar muscle atrophy.

In one embodiment, the subject methods and algorithm are used to diagnose, characterize, and monitor thyroid cancer. Other types of cancer that can be diagnosed, characterized and/or monitored using the algorithms and methods of the present invention include but are not limited to adrenal cortical cancer, anal cancer, aplastic anemia, bile duct cancer, bladder cancer, bone cancer, bone metastasis, central nervous system (CNS) cancers, peripheral nervous system (PNS) cancers, breast cancer, Castleman's disease, cervical cancer, childhood Non-Hodgkin's lymphoma, colon and rectum cancer, endometrial cancer, esophagus cancer, Ewing's family of tumors (e.g. Ewing's sarcoma), eye cancer, gallbladder cancer, gastrointestinal carcinoid tumors, gastrointestinal stromal tumors, gestational trophoblastic disease, hairy cell leukemia, Hodgkin's disease, Kaposi's sarcoma, kidney cancer, laryngeal and hypopharyngeal cancer, acute lymphocytic leukemia, acute myeloid leukemia, children's leukemia, chronic lymphocytic leukemia, chronic myeloid leukemia, liver cancer, lung cancer, lung carcinoid tumors, Non-Hodgkin's lymphoma, male breast cancer, malignant mesothelioma, multiple myeloma, myelodysplastic syndrome, myeloproliferative disorders, nasal cavity and paranasal cancer, nasopharyngeal cancer, neuroblastoma, oral cavity and oropharyngeal cancer, osteosarcoma, ovarian cancer, pancreatic cancer, penile cancer, pituitary tumor, prostate cancer, retinoblastoma, rhabdomyosarcoma, salivary gland cancer, sarcoma (adult soft tissue cancer), melanoma skin cancer, non-melanoma skin cancer, stomach cancer, testicular cancer, thymus cancer, uterine cancer (e.g. uterine sarcoma), vaginal cancer, vulvar cancer, and Waldenstrom's macroglobulinemia.

In accordance with the foregoing, the presence of such multiple copies of a gene, or genes, as disclosed herein may be determined using northern or southern blotting and employing the sequences disclosed herein to develop probes for this purpose. Such probes may be composed of DNA or RNA or synthetic nucleotides or a combination of the above and may advantageously be comprised of a contiguous stretch of nucleotide residues matching, or complementary to, a gene or sequence as identified in Table 1, 3, 4, 5, 6, or 8, or lists 1-45. Such probes will most usefully comprise a contiguous stretch of at least 15, preferably at least 30, more preferably at least 50, most preferably at least 80, and especially at least 100, even 200 residues, derived from one or more of the sequences as identified in Table 1, 3, 4, 5, 6, or 8, or lists 1-45. Thus, where a single probe binds multiple times to the genome of a sample of cells that are cancerous, or are suspected of being cancerous, or predisposed to become cancerous, whereas binding of the same probe to a similar amount of DNA derived from the genome of otherwise non-cancerous cells of the same organ or tissue results in observably less binding, this is indicative of the presence of multiple copies of a gene comprising, or corresponding to, the sequence as identified in Table 1, 3, 4, 5, 6, or 8, or lists 1-45 from which the probe sequenced was derived.

In one such embodiment, the elevated expression, as compared to normal cells and/or tissues of the same organ, is determined by measuring the relative rates of transcription of RNA, such as by production of corresponding cDNAs and then analyzing the resulting DNA using probes developed from the gene sequences as identified in Table 1, 3, 4, 5, 6, or 8, or lists 1-45. Thus, the levels of cDNA produced by use of reverse transcriptase with the full RNA complement of a cell suspected of being cancerous produces a corresponding amount of cDNA that can then be amplified using polymerase chain reaction, or some other means, such as rolling circle amplification, to determine the relative levels of resulting cDNA and, thereby, the relative levels of gene expression.

Increased expression may also be determined using agents that selectively bind to, and thereby detect, the presence of expression products of the genes disclosed herein. For example, an antibody, possibly a suitably labeled antibody, such as where the antibody is bound to a fluorescent or radiolabel, may be generated against one of the polypeptides comprising a sequence as identified in Table 1, 3, 4, 5, 6, or 8, or lists 1-45, and said antibody will then react with, binding either selectively or specifically, to a polypeptide encoded by one of the genes that corresponds to a sequence disclosed herein. Such antibody binding, especially relative extent of such binding in samples derived from suspected cancerous, as opposed to otherwise non-cancerous, cells and tissues, can then be used as a measure of the extent of expression, or over-expression, of the cancer-related genes identified herein. Thus, the genes identified herein as being over-expressed in cancerous cells and tissues may be over-expressed due to increased copy number, or due to over-transcription, such as where the over-expression is due to over-production of a transcription factor that activates the gene and leads to repeated binding of RNA polymerase, thereby generating large than normal amounts of RNA transcripts, which are subsequently translated into polypeptides, such as the polypeptides comprising amino acid sequences as identified in Table 1, 3, 4, 5, 6, or 8, or lists 1-45. Such analysis provides an additional means of ascertaining the expression of the genes identified according to the invention and thereby determining the presence of a cancerous state in a sample derived from a patient to be tested, of the predisposition to develop cancer at a subsequent time in said patient.

In employing the methods of the invention, it should be borne in mind that gene expression indicative of a cancerous state need not be characteristic of every cell found to be cancerous. Thus, the methods disclosed herein are useful for detecting the presence of a cancerous condition within a tissue where less than all cells exhibit the complete pattern of over-expression. For example, a set of selected genes, comprising sequences homologous under stringent conditions, or at least 90%, preferably 95%, identical to at least one of the sequences as identified in Table 1, 3, 4, 5, 6, or 8, or lists 1-45, may be found, using appropriate probes, either DNA or RNA, to be present in as little as 60% of cells derived from a sample of tumorous, or malignant, tissue while being absent from as much as 60% of cells derived from corresponding non-cancerous, or otherwise normal, tissue (and thus being present in as much as 40% of such normal tissue cells). In one embodiment, such gene pattern is found to be present in at least 70% of cells drawn from a cancerous tissue and absent from at least 70% of a corresponding normal, non-cancerous, tissue sample. In another embodiment, such gene pattern is found to be present in at least 80% of cells drawn from a cancerous tissue and absent from at least 80% of a corresponding normal, non-cancerous, tissue sample. In another embodiment, such gene pattern is found to be present in at least 90% of cells drawn from a cancerous tissue and absent from at least 90% of a corresponding normal, non-cancerous, tissue sample. In another embodiment, such gene pattern is found to be present in at least 100% of cells drawn from a cancerous tissue and absent from at least 100% of a corresponding normal, non-cancerous, tissue sample, although the latter embodiment may represent a rare occurrence.

Methods of Detecting Thyroid Neoplasms

The methods of the present invention provide for diagnosis of diseases or conditions of a subject by use of molecular profiling. In some cases, the methods of the present invention provide for diagnosis of diseases or conditions of a subject by the use of molecular profiling in combination with other methods known in the art such as, for example, cytological analysis, or immuno-histochemistry. As used herein, the term subject refers to any animal (e.g. a mammal), including but not limited to humans, non-human primates, rodents, dogs, pigs, and the like. The subject may or may not be aware of the disease or condition.

In some embodiments molecular profiling includes detection, analysis, or quantification of nucleic acid (DNA, or RNA), protein, or a combination thereof. The diseases or conditions to be diagnosed by the methods of the present invention include for example conditions of abnormal growth in one or more tissues of a subject including but not limited to skin, heart, lung, kidney, breast, pancreas, liver, muscle, smooth muscle, bladder, gall bladder, colon, intestine, brain, esophagus, or prostate. In some embodiments, the tissues analyzed by the methods of the present invention include thyroid tissues.

Biological samples may be treated to extract nucleic acid such as DNA or RNA. The nucleic acid may be contacted with an array of probes of the present invention under conditions to allow hybridization. The degree of hybridization may be assayed in a quantitative matter using a number of methods known in the art. In some cases, the degree of hybridization at a probe position may be related to the intensity of signal provided by the assay, which therefore is related to the amount of complementary nucleic acid sequence present in the sample. Software can be used to extract, normalize, summarize, and analyze array intensity data from probes across the human genome or transcriptome including expressed genes, exons, introns, and miRNAs. In some embodiments, the intensity of a given probe in either the benign or malignant samples can be compared against a reference set to determine whether differential expression is occurring in a sample. An increase or decrease in relative intensity at a marker position on an array corresponding to an expressed sequence is indicative of an increase or decrease respectively of expression of the corresponding expressed sequence. Alternatively, a decrease in relative intensity may be indicative of a mutation in the expressed sequence.

The resulting intensity values for each sample can be analyzed using feature selection techniques including filter techniques which assess the relevance of features by looking at the intrinsic properties of the data, wrapper methods which embed the model hypothesis within a feature subset search, and embedded techniques in which the search for an optimal set of features is built into a classifier algorithm.

Filter techniques useful in the methods of the present invention include (1) parametric methods such as the use of two sample t-tests, ANOVA analyses, Bayesian frameworks, and Gamma distribution models (2) model free methods such as the use of Wilcoxon rank sum tests, between-within class sum of squares tests, rank products methods, random permutation methods, or TNoM which involves setting a threshold point for fold-change differences in expression between two datasets and then detecting the threshold point in each gene that minimizes the number of missclassifications (3) and multivariate methods such as bivariate methods, correlation based feature selection methods (CFS), minimum redundancy maximum relavance methods (MRMR), Markov blanket filter methods, and uncorrelated shrunken centroid methods. Wrapper methods useful in the methods of the present invention include sequential search methods, genetic algorithms, and estimation of distribution algorithms. Embedded methods useful in the methods of the present invention include random forest algorithms, weight vector of support vector machine algorithms, and weights of logistic regression algorithms. Bioinformatics. 2007 Oct. 1; 23(19):2507-17 provides an overview of the relative merits of the filter techniques provided above for the analysis of intensity data.

Selected features may then be classified using a classifier algorithm. Illustrative algorithms include but are not limited to methods that reduce the number of variables such as principal component analysis algorithms, partial least squares methods, and independent component analysis algorithms. Illustrative algorithms further include but are not limited to methods that handle large numbers of variables directly such as statistical methods and methods based on machine learning techniques. Statistical methods include penalized logistic regression, prediction analysis of microarrays (PAM), methods based on shrunken centroids, support vector machine analysis, and regularized linear discriminant analysis. Machine learning techniques include bagging procedures, boosting procedures, random forest algorithms, and combinations thereof. Cancer Inform. 2008; 6: 77-97 provides an overview of the classification techniques provided above for the analysis of microarray intensity data.

The markers and genes of the present invention can be utilized to characterize the cancerous or non-cancerous status of cells or tissues. The present invention includes a method for diagnosing benign tissues or cells from malignant tissues or cells comprising determining the differential expression of a marker or gene in a thyroid sample of a subject wherein said marker or gene is a marker or gene listed in Table 1, 3, 4, 5, 6, or 8, or lists 1-45. The present invention also includes methods for diagnosing medullary thyroid carcinoma comprising determining the differential expression of a marker or gene in a thyroid sample of a subject. The present invention also includes methods for diagnosing thyroid pathology subtypes comprising determining the differential expression of a marker or gene in a thyroid sample of a subject.

In some embodiments, the diseases or conditions diagnosed by the methods of the present invention include benign and malignant hyperproliferative disorders including but not limited to cancers, hyperplasias, or neoplasias. In some cases, the hyperproliferative disorders diagnosed by the methods of the present invention include but are not limited to breast cancer such as a ductal carcinoma in duct tissue in a mammary gland, medullary carcinomas, colloid carcinomas, tubular carcinomas, and inflammatory breast cancer; ovarian cancer, including epithelial ovarian tumors such as adenocarcinoma in the ovary and an adenocarcinoma that has migrated from the ovary into the abdominal cavity; uterine cancer; cervical cancer such as adenocarcinoma in the cervix epithelial including squamous cell carcinoma and adenocarcinomas; prostate cancer, such as a prostate cancer selected from the following: an adenocarcinoma or an adenocarinoma that has migrated to the bone; pancreatic cancer such as epitheliod carcinoma in the pancreatic duct tissue and an adenocarcinoma in a pancreatic duct; bladder cancer such as a transitional cell carcinoma in urinary bladder, urothelial carcinomas (transitional cell carcinomas), tumors in the urothelial cells that line the bladder, squamous cell carcinomas, adenocarcinomas, and small cell cancers; leukemia such as acute myeloid leukemia (AML), acute lymphocytic leukemia, chronic lymphocytic leukemia, chronic myeloid leukemia, hairy cell leukemia, myelodysplasia, myeloproliferative disorders, acute myelogenous leukemia (AML), chronic myelogenous leukemia (CML), mastocytosis, chronic lymphocytic leukemia (CLL), multiple myeloma (MM), and myelodysplastic syndrome (MDS); bone cancer; lung cancer such as non-small cell lung cancer (NSCLC), which is divided into squamous cell carcinomas, adenocarcinomas, and large cell undifferentiated carcinomas, and small cell lung cancer; skin cancer such as basal cell carcinoma, melanoma, squamous cell carcinoma and actinic keratosis, which is a skin condition that sometimes develops into squamous cell carcinoma; eye retinoblastoma; cutaneous or intraocular (eye) melanoma; primary liver cancer (cancer that begins in the liver); kidney cancer; AIDS-related lymphoma such as diffuse large B-cell lymphoma, B-cell immunoblastic lymphoma and small non-cleaved cell lymphoma; Kaposi's Sarcoma; viral-induced cancers including hepatitis B virus (HBV), hepatitis C virus (HCV), and hepatocellular carcinoma; human lymphotropic virus-type 1 (HTLV-1) and adult T-cell leukemia/lymphoma; and human papilloma virus (HPV) and cervical cancer; central nervous system cancers (CNS) such as primary brain tumor, which includes gliomas (astrocytoma, anaplastic astrocytoma, or glioblastoma multiforme), Oligodendroglioma, Ependymoma, Meningioma, Lymphoma, Schwannoma, and Medulloblastoma; peripheral nervous system (PNS) cancers such as acoustic neuromas and malignant peripheral nerve sheath tumor (MPNST) including neurofibromas and schwannomas, malignant fibrous cytoma, malignant fibrous histiocytoma, malignant meningioma, malignant mesothelioma, and malignant mixed Müllerian tumor; oral cavity and oropharyngeal cancer such as, hypopharyngeal cancer, laryngeal cancer, nasopharyngeal cancer, and oropharyngeal cancer; stomach cancer such as lymphomas, gastric stromal tumors, and carcinoid tumors; testicular cancer such as germ cell tumors (GCTs), which include seminomas and nonseminomas, and gonadal stromal tumors, which include Leydig cell tumors and Sertoli cell tumors; thymus cancer such as to thymomas, thymic carcinomas, Hodgkin disease, non-Hodgkin lymphomas carcinoids or carcinoid tumors; rectal cancer; and colon cancer. In some cases, the diseases or conditions diagnosed by the methods of the present invention include but are not limited to thyroid disorders such as for example benign thyroid disorders including but not limited to follicular adenomas, Hurthle cell adenomas, lymphocytic throiditis, and thyroid hyperplasia. In some cases, the diseases or conditions diagnosed by the methods of the present invention include but are not limited to malignant thyroid disorders such as for example follicular carcinomas, follicular variant of papillary thyroid carcinomas, and papillary carcinomas. In some cases, the methods of the present invention provide for a diagnosis of a tissue as diseased or normal. In other cases, the methods of the present invention provide for a diagnosis of normal, benign, or malignant. In some cases, the methods of the present invention provide for a diagnosis of benign/normal, or malignant. In some cases, the methods of the present invention provide for a diagnosis of one or more of the specific diseases or conditions provided herein.

I. Obtaining a Biological Sample

In some embodiments, the methods of the present invention provide for obtaining a sample from a subject. As used herein, the term subject refers to any animal (e.g. a mammal), including but not limited to humans, non-human primates, rodents, dogs, pigs, and the like. The methods of obtaining provided herein include methods of biopsy including fine needle aspiration, core needle biopsy, vacuum assisted biopsy, incisional biopsy, excisional biopsy, punch biopsy, shave biopsy or skin biopsy. The sample may be obtained from any of the tissues provided herein including but not limited to skin, heart, lung, kidney, breast, pancreas, liver, muscle, smooth muscle, bladder, gall bladder, colon, intestine, brain, prostate, esophagus, or thyroid. Alternatively, the sample may be obtained from any other source including but not limited to blood, sweat, hair follicle, buccal tissue, tears, menses, feces, or saliva. In some embodiments of the present invention, a medical professional may obtain a biological sample for testing.

The sample may be obtained by methods known in the art such as the biopsy methods provided herein, swabbing, scraping, phlebotomy, or any other methods known in the art. In some cases, the sample may be obtained, stored, or transported using components of a kit of the present invention. In some cases, multiple samples, such as multiple thyroid samples may be obtained for diagnosis by the methods of the present invention. In some cases, multiple samples, such as one or more samples from one tissue type (e.g. thyroid) and one or more samples from another tissue (e.g. buccal) may be obtained for diagnosis by the methods of the present invention. In some cases, multiple samples such as one or more samples from one tissue type (e.g. thyroid) and one or more samples from another tissue (e.g. buccal) may be obtained at the same or different times. In some cases, the samples obtained at different times are stored and/or analyzed by different methods. For example, a sample may be obtained and analyzed by cytological analysis (routine staining). In some cases, further sample may be obtained from a subject based on the results of a cytological analysis. The diagnosis of cancer may include an examination of a subject by a physician, nurse or other medical professional. The examination may be part of a routine examination, or the examination may be due to a specific complaint including but not limited to one of the following: pain, illness, anticipation of illness, presence of a suspicious lump or mass, a disease, or a condition. The subject may or may not be aware of the disease or condition. The medical professional may obtain a biological sample for testing. In some cases the medical professional may refer the subject to a testing center or laboratory for submission of the biological sample.

In some cases the medical professional may refer the subject to a testing center or laboratory for submission of the biological sample. In other cases, the subject may provide the sample. In some cases, a molecular profiling business of the present invention may obtain the sample. The sample may be obtained by methods known in the art such as the biopsy methods provided herein, swabbing, scraping, phlebotomy, or any other methods known in the art. In some cases, the sample may be obtained, stored, or transported using components of a kit of the present invention. In some cases, multiple samples, such as multiple thyroid samples may be obtained for diagnosis by the methods of the present invention. In some cases, multiple samples, such as one or more samples from one tissue type (e.g. thyroid) and one or more samples from another tissue (e.g. buccal) may be obtained for diagnosis by the methods of the present invention. In some cases, multiple samples such as one or more samples from one tissue type (e.g. thyroid) and one or more samples from another tissue (e.g. buccal) may be obtained at the same or different times. In some cases, the samples obtained at different times are stored and/or analyzed by different methods. For example, a sample may be obtained and analyzed by cytological analysis (routine staining). In some cases, further sample may be obtained from a subject based on the results of a cytological analysis.

In some cases, the subject may be referred to a specialist such as an oncologist, surgeon, or endocrinologist for further diagnosis. The specialist may likewise obtain a biological sample for testing or refer the individual to a testing center or laboratory for submission of the biological sample. In any case, the biological sample may be obtained by a physician, nurse, or other medical professional such as a medical technician, endocrinologist, cytologist, phlebotomist, radiologist, or a pulmonologist. The medical professional may indicate the appropriate test or assay to perform on the sample, or the molecular profiling business of the present disclosure may consult on which assays or tests are most appropriately indicated. The molecular profiling business may bill the individual or medical or insurance provider thereof for consulting work, for sample acquisition and or storage, for materials, or for all products and services rendered.

In some embodiments of the present invention, a medical professional need not be involved in the initial diagnosis or sample acquisition. An individual may alternatively obtain a sample through the use of an over the counter kit. Said kit may contain a means for obtaining said sample as described herein, a means for storing said sample for inspection, and instructions for proper use of the kit. In some cases, molecular profiling services are included in the price for purchase of the kit. In other cases, the molecular profiling services are billed separately.

A sample suitable for use by the molecular profiling business may be any material containing tissues, cells, nucleic acids, genes, gene fragments, expression products, gene expression products, or gene expression product fragments of an individual to be tested. Methods for determining sample suitability and/or adequacy are provided. A sample may include but is not limited to, tissue, cells, or biological material from cells or derived from cells of an individual. The sample may be a heterogeneous or homogeneous population of cells or tissues. The biological sample may be obtained using any method known to the art that can provide a sample suitable for the analytical methods described herein.

The sample may be obtained by non-invasive methods including but not limited to: scraping of the skin or cervix, swabbing of the cheek, saliva collection, urine collection, feces collection, collection of menses, tears, or semen. In other cases, the sample is obtained by an invasive procedure including but not limited to: biopsy, alveolar or pulmonary lavage, needle aspiration, or phlebotomy. The method of needle aspiration may further include fine needle aspiration, core needle biopsy, vacuum assisted biopsy, or large core biopsy. In some embodiments, multiple samples may be obtained by the methods herein to ensure a sufficient amount of biological material. Methods of obtaining suitable samples of thyroid are known in the art and are further described in the ATA Guidelines for thryoid nodule management (Cooper et al. Thyroid Vol. 16 No. 2 2006), herein incorporated by reference in its entirety. Generic methods for obtaining biological samples are also known in the art and further described in for example Ramzy, Ibrahim Clinical Cytopathology and Aspiration Biopsy 2001 which is herein incorporated by reference in its entirety. In one embodiment, the sample is a fine needle aspirate of a thyroid nodule or a suspected thyroid tumor. In some cases, the fine needle aspirate sampling procedure may be guided by the use of an ultrasound, X-ray, or other imaging device.

In some embodiments of the present invention, the molecular profiling business may obtain the biological sample from a subject directly, from a medical professional, from a third party, or from a kit provided by the molecular profiling business or a third party. In some cases, the biological sample may be obtained by the molecular profiling business after the subject, a medical professional, or a third party acquires and sends the biological sample to the molecular profiling business. In some cases, the molecular profiling business may provide suitable containers, and excipients for storage and transport of the biological sample to the molecular profiling business.

II. Storing the Sample

In some embodiments, the methods of the present invention provide for storing the sample for a time such as seconds, minutes, hours, days, weeks, months, years or longer after the sample is obtained and before the sample is analyzed by one or more methods of the invention. In some cases, the sample obtained from a subject is subdivided prior to the step of storage or further analysis such that different portions of the sample are subject to different downstream methods or processes including but not limited to storage, cytological analysis, adequacy tests, nucleic acid extraction, molecular profiling or a combination thereof.

In some cases, a portion of the sample may be stored while another portion of said sample is further manipulated. Such manipulations may include but are not limited to molecular profiling; cytological staining; gene or gene expression product (RNA or protein) extraction, detection, or quantification; fixation; and examination. In other cases, the sample is obtained and stored and subdivided after the step of storage for further analysis such that different portions of the sample are subject to different downstream methods or processes including but not limited to storage, cytological analysis, adequacy tests, nucleic acid extraction, molecular profiling or a combination thereof. In some cases, samples are obtained and analyzed by for example cytological analysis, and the resulting sample material is further analyzed by one or more molecular profiling methods of the present invention. In such cases, the samples may be stored between the steps of cytological analysis and the steps of molecular profiling. Samples may be stored upon acquisition to facilitate transport, or to wait for the results of other analyses. In another embodiment, samples may be stored while awaiting instructions from a physician or other medical professional.

The acquired sample may be placed in a suitable medium, excipient, solution, or container for short term or long term storage. Said storage may require keeping the sample in a refrigerated, or frozen environment. The sample may be quickly frozen prior to storage in a frozen environment. The frozen sample may be contacted with a suitable cryopreservation medium or compound including but not limited to: glycerol, ethylene glycol, sucrose, or glucose. A suitable medium, excipient, or solution may include but is not limited to: hanks salt solution, saline, cellular growth medium, an ammonium salt solution such as ammonium sulphate or ammonium phosphate, or water. Suitable concentrations of ammonium salts include solutions of about 0.1 g/ml, 0.2 g/ml, 0.3 g/ml, 0.4 g/ml, 0.5 g/ml, 0.6 g/ml, 0.7 g/ml, 0.8 g/ml, 0.9 g/ml, 1.0 g/ml, 1.1 g/ml, 1.2 g/ml, 1.3 g/ml, 1.4 g/ml, 1.5 g/ml, 1.6 g/ml, 1.7 g/ml, 1.8 g/ml, 1.9 g/ml, 2.0 g/ml, 2.2 g/ml, 2.3 g/ml, 2.5 g/ml or higher. The medium, excipient, or solution may or may not be sterile.

The sample may be stored at room temperature or at reduced temperatures such as cold temperatures (e.g. between about 20° C. and about 0° C.), or freezing temperatures, including for example 0 C, −1 C, −2 C, −3 C, −4 C, −5 C, −6 C, −7 C, −8 C, −9 C, −10 C, −12 C, −14 C, −15 C, −16 C, −20 C, −22 C, −25 C, −28 C, −30 C, −35 C, −40 C, −45 C, −50 C, −60 C, −70 C, −80 C, −100 C, −120 C, −140 C, −180 C, −190 C, or about −200 C. In some cases, the samples may be stored in a refrigerator, on ice or a frozen gel pack, in a freezer, in a cryogenic freezer, on dry ice, in liquid nitrogen, or in a vapor phase equilibrated with liquid nitrogen.

The medium, excipient, or solution may contain preservative agents to maintain the sample in an adequate state for subsequent diagnostics or manipulation, or to prevent coagulation. Said preservatives may include citrate, ethylene diamine tetraacetic acid, sodium azide, or thimersol. The sample may be fixed prior to or during storage by any method known to the art such as using glutaraldehyde, formaldehyde, or methanol. The container may be any container suitable for storage and or transport of the biological sample including but not limited to: a cup, a cup with a lid, a tube, a sterile tube, a vacuum tube, a syringe, a bottle, a microscope slide, or any other suitable container. The container may or may not be sterile. In some cases, the sample may be stored in a commercial preparation suitable for storage of cells for subsequent cytological analysis such as but not limited to Cytyc ThinPrep, SurePath, or Monoprep.

The sample container may be any container suitable for storage and or transport of the biological sample including but not limited to: a cup, a cup with a lid, a tube, a sterile tube, a vacuum tube, a syringe, a bottle, a microscope slide, or any other suitable container. The container may or may not be sterile.

III. Transportation of the Sample

The methods of the present invention provide for transport of the sample. In some cases, the sample is transported from a clinic, hospital, doctor's office, or other location to a second location whereupon the sample may be stored and/or analyzed by for example, cytological analysis or molecular profiling. In some cases, the sample may be transported to a molecular profiling company in order to perform the analyses described herein. In other cases, the sample may be transported to a laboratory such as a laboratory authorized or otherwise capable of performing the methods of the present invention such as a Clinical Laboratory Improvement Amendments (CLIA) laboratory. The sample may be transported by the individual from whom the sample derives. Said transportation by the individual may include the individual appearing at a molecular profiling business or a designated sample receiving point and providing a sample. Said providing of the sample may involve any of the techniques of sample acquisition described herein, or the sample may have already have been acquired and stored in a suitable container as described herein. In other cases the sample may be transported to a molecular profiling business using a courier service, the postal service, a shipping service, or any method capable of transporting the sample in a suitable manner. In some cases, the sample may be provided to a molecular profiling business by a third party testing laboratory (e.g. a cytology lab). In other cases, the sample may be provided to a molecular profiling business by the subject's primary care physician, endocrinologist or other medical professional. The cost of transport may be billed to the individual, medical provider, or insurance provider. The molecular profiling business may begin analysis of the sample immediately upon receipt, or may store the sample in any manner described herein. The method of storage may or may not be the same as chosen prior to receipt of the sample by the molecular profiling business.

The sample may be transported in any medium or excipient including any medium or excipient provided herein suitable for storing the sample such as a cryopreservation medium or a liquid based cytology preparation. In some cases, the sample may be transported frozen or refrigerated such as at any of the suitable sample storage temperatures provided herein.

Upon receipt of the sample by the molecular profiling business, a representative or licensee thereof, a medical professional, researcher, or a third party laboratory or testing center (e.g. a cytology laboratory) the sample may be assayed using a variety of routine analyses known to the art such as cytological assays, and genomic analysis. Such tests may be indicative of cancer, the type of cancer, any other disease or condition, the presence of disease markers, or the absence of cancer, diseases, conditions, or disease markers. The tests may take the form of cytological examination including microscopic examination as described below. The tests may involve the use of one or more cytological stains. The biological material may be manipulated or prepared for the test prior to administration of the test by any suitable method known to the art for biological sample preparation. The specific assay performed may be determined by the molecular profiling company, the physician who ordered the test, or a third party such as a consulting medical professional, cytology laboratory, the subject from whom the sample derives, or an insurance provider. The specific assay may be chosen based on the likelihood of obtaining a definite diagnosis, the cost of the assay, the speed of the assay, or the suitability of the assay to the type of material provided.

IV. Test for Adequacy

Subsequent to or during sample acquisition, including before or after a step of storing the sample, the biological material may be collected and assessed for adequacy, for example, to asses the suitability of the sample for use in the methods and compositions of the present invention. The assessment may be performed by the individual who obtains the sample, the molecular profiling business, the individual using a kit, or a third party such as a cytological lab, pathologist, endocrinologist, or a researcher. The sample may be determined to be adequate or inadequate for further analysis due to many factors including but not limited to: insufficient cells, insufficient genetic material, insufficient protein, DNA, or RNA, inappropriate cells for the indicated test, or inappropriate material for the indicated test, age of the sample, manner in which the sample was obtained, or manner in which the sample was stored or transported. Adequacy may be determined using a variety of methods known in the art such as a cell staining procedure, measurement of the number of cells or amount of tissue, measurement of total protein, measurement of nucleic acid, visual examination, microscopic examination, or temperature or pH determination. In one embodiment, sample adequacy will be determined from the results of performing a gene expression product level analysis experiment. In another embodiment sample adequacy will be determined by measuring the content of a marker of sample adequacy. Such markers include elements such as iodine, calcium, magnesium, phosphorous, carbon, nitrogen, sulfur, iron etc.; proteins such as but not limited to thyroglobulin; cellular mass; and cellular components such as protein, nucleic acid, lipid, or carbohydrate.

In some cases, iodine may be measured by a chemical method such as described in U.S. Pat. No. 3,645,691 which is incorporated herein by reference in its entirety or other chemical methods known in the art for measuring iodine content. Chemical methods for iodine measurement include but are not limited to methods based on the Sandell and Kolthoff reaction. Said reaction proceeds according to the following equation:

2Ce⁴⁺+As³+→2Ce³⁺+As⁵+I.

Iodine has a catalytic effect upon the course of the reaction, i.e., the more iodine present in the preparation to be analyzed, the more rapidly the reaction proceeds. The speed of reaction is proportional to the iodine concentration. In some cases, this analytical method may carried out in the following manner:

A predetermined amount of a solution of arsenous oxide As₂O₃ in concentrated sulfuric or nitric acid is added to the biological sample and the temperature of the mixture is adjusted to reaction temperature, i.e., usually to a temperature between 20° C. and 60° C. A predetermined amount of a cerium (IV) sulfate solution in sulfuric or nitric acid is added thereto. Thereupon, the mixture is allowed to react at the predetermined temperature for a definite period of time. Said reaction time is selected in accordance with the order of magnitude of the amount of iodine to be determined and with the respective selected reaction temperature. The reaction time is usually between about 1 minute and about 40 minutes. Thereafter, the content of the test solution of cerium (IV) ions is determined photometrically. The lower the photometrically determined cerium (IV) ion concentration is, the higher is the speed of reaction and, consequently, the amount of catalytic agent, i.e., of iodine. In this manner the iodine of the sample can directly and quantitatively be determined.

In other cases, iodine content of a sample of thyroid tissue may be measured by detecting a specific isotope of iodine such as for example ¹²³I, ¹²⁴I, ¹²⁵I, and ¹³¹I. In still other cases, the marker may be another radioisotope such as an isotope of carbon, nitrogen, sulfur, oxygen, iron, phosphorous, or hydrogen. The radioisotope in some instances may be administered prior to sample collection. Methods of radioisotope administration suitable for adequacy testing are well known in the art and include injection into a vein or artery, or by ingestion. A suitable period of time between administration of the isotope and acquisition of thyroid nodule sample so as to effect absorption of a portion of the isotope into the thyroid tissue may include any period of time between about a minute and a few days or about one week including about 1 minute, 2 minutes, 5 minutes, 10 minutes, 15 minutes, ½ an hour, an hour, 8 hours, 12 hours, 24 hours, 48 hours, 72 hours, or about one, one and a half, or two weeks, and may readily be determined by one skilled in the art. Alternatively, samples may be measured for natural levels of isotopes such as radioisotopes of iodine, calcium, magnesium, carbon, nitrogen, sulfur, oxygen, iron, phosphorous, or hydrogen.

(i) Cell and/or Tissue Content Adequacy Test

Methods for determining the amount of a tissue include but are not limited to weighing the sample or measuring the volume of sample. Methods for determining the amount of cells include but are not limited to counting cells which may in some cases be performed after dis-aggregation with for example an enzyme such as trypsin or collagenase or by physical means such as using a tissue homogenizer for example. Alternative methods for determining the amount of cells recovered include but are not limited to quantification of dyes that bind to cellular material, or measurement of the volume of cell pellet obtained following centrifugation. Methods for determining that an adequate number of a specific type of cell is present include PCR, Q-PCR, RT-PCR, immuno-histochemical analysis, cytological analysis, microscopic, and or visual analysis.

(ii) Nucleic Acid Content Adequacy Test

Samples may be analyzed by determining nucleic acid content after extraction from the biological sample using a variety of methods known to the art. In some cases, RNA or mRNA is extracted from other nucleic acids prior to nucleic acid content analysis. Nucleic acid content may be extracted, purified, and measured by ultraviolet absorbance, including but not limited to aborbance at 260 nanometers using a spectrophotometer. In other cases nucleic acid content or adequacy may be measured by fluorometer after contacting the sample with a stain. In still other cases, nucleic acid content or adequacy may be measured after electrophoresis, or using an instrument such as an agilent bioanalyzer for example. It is understood that the methods of the present invention are not limited to a specific method for measuring nucleic acid content and or integrity.

In some embodiments, the RNA quantity or yield from a given sample is measured shortly after purification using a NanoDrop spectrophotometer in a range of nano- to micrograms. In some embodiments, RNA quality is measured using an Agilent 2100 Bioanalyzer instrument, and is characterized by a calculated RNA Integrity Number (RIN, 1-10). The NanoDrop is a cuvette-free spectrophotometer. It uses 1 microleter to measure from 5 ng/μl to 3,000 ng/μl of sample. The key features of NanoDrop include low volume of sample and no cuvette; large dynamic range 5 ng/μ1 to 3,000 ng/μl; and it allows quantitation of DNA, RNA and proteins. NanoDrop™ 2000c allows for the analysis of 0.5 μl-2.0 μl samples, without the need for cuvettes or capillaries.

RNA quality can be measured by a calculated RNA Integrity Number (RIN). The RNA integrity number

(RIN) is an algorithm for assigning integrity values to RNA measurements. The integrity of RNA is a major concern for gene expression studies and traditionally has been evaluated using the 28S to 18S rRNA ratio, a method that has been shown to be inconsistent. The RIN algorithm is applied to electrophoretic RNA measurements and based on a combination of different features that contribute information about the RNA integrity to provide a more robust universal measure. In some embodiments, RNA quality is measured using an Agilent 2100 Bioanalyzer instrument. The protocols for measuring RNA quality are known and available commercially, for example, at Agilent website. Briefly, in the first step, researchers deposit total RNA sample into an RNA Nano LabChip. In the second step, the LabChip is inserted into the Agilent bioanalyzer and let the analysis run, generating a digital electropherogram. In the third step, the new RIN algorithm then analyzes the entire electrophoretic trace of the RNA sample, including the presence or absence of degradation products, to determine sample integrity. Then, The algorithm assigns a 1 to 10 RIN score, where level 10 RNA is completely intact. Because interpretation of the electropherogram is automatic and not subject to individual interpretation, universal and unbiased comparison of samples is enabled and repeatability of experiments is improved. The RIN algorithm was developed using neural networks and adaptive learning in conjunction with a large database of eukaryote total RNA samples, which were obtained mainly from human, rat, and mouse tissues. Advantages of RIN include obtain a numerical assessment of the integrity of RNA; directly comparing RNA samples, e.g. before and after archival, compare integrity of same tissue across different labs; and ensuring repeatability of experiments, e.g. if RIN shows a given value and is suitable for microarray experiments, then the RIN of the same value can always be used for similar experiments given that the same organism/tissue/extraction method is used (Schroeder A, et al. BMC Molecular Biology 2006, 7:3 (2006)).

In some embodiments, RNA quality is measured on a scale of RIN 1 to 10, 10 being highest quality. In one aspect, the present invention provides a method of analyzing gene expression from a sample with an RNA RIN value equal or less than 6.0. In some embodiments, a sample containing RNA with an RIN number of 1.0, 2.0, 3.0, 4.0, 5.0 or 6.0 is analyzed for microarray gene expression using the subject methods and algorithms of the present invention. In some embodiments, the sample is a fine needle aspirate of thyroid tissue. The sample can be degraded with an RIN as low as 2.0.

Determination of gene expression in a given sample is a complex, dynamic, and expensive process. RNA samples with RIN are typically not used for multi-gene microarray analysis, and may instead be used only for single-gene RT-PCR and/or TaqMan assays. This dichotomy in the usefulness of RNA according to quality has thus far limited the usefulness of samples and hampered research efforts. The present invention provides methods via which low quality RNA can be used to obtain meaningful multi-gene expression results from samples containing low concentrations of RNA, for example, thyroid FNA samples.

In addition, samples having a low and/or un-measurable RNA concentration by NanoDrop normally deemed inadequate for multi-gene expression profiling can be measured and analyzed using the subject methods and algorithms of the present invention. The most sensitive and “state of the art” apparatus used to measure nucleic acid yield in the laboratory today is the NanoDrop spectrophotometer. Like many quantitative instruments of its kind, the accuracy of a NanoDrop measurement decreases significantly with very low RNA concentration. The minimum amount of RNA necessary for input into a microarray experiment also limits the usefulness of a given sample. In the present invention, a sample containing a very low amount of nucleic acid can be estimated using a combination of the measurements from both the NanoDrop and the Bioanalyzer instruments, thereby optimizing the sample for multi-gene expression assays and analysis.

(iii) Protein Content Adequacy Test

In some cases, protein content in the biological sample may be measured using a variety of methods known to the art, including but not limited to: ultraviolet absorbance at 280 nanometers, cell staining as described herein, or protein staining with for example coomassie blue, or bichichonic acid. In some cases, protein is extracted from the biological sample prior to measurement of the sample. In some cases, multiple tests for adequacy of the sample may be performed in parallel, or one at a time. In some cases, the sample may be divided into aliquots for the purpose of performing multiple diagnostic tests prior to, during, or after assessing adequacy. In some cases, the adequacy test is performed on a small amount of the sample which may or may not be suitable for further diagnostic testing. In other cases, the entire sample is assessed for adequacy. In any case, the test for adequacy may be billed to the subject, medical provider, insurance provider, or government entity.

In some embodiments of the present invention, the sample may be tested for adequacy soon or immediately after collection. In some cases, when the sample adequacy test does not indicate a sufficient amount sample or sample of sufficient quality, additional samples may be taken.

V. Cytological Analysis

Samples may be analyzed by cell staining combined with microscopic examination of the cells in the biological sample. Cell staining, or cytological examination, may be performed by a number of methods and suitable reagents known to the art including but not limited to: EA stains, hematoxylin stains, cytostain, papanicolaou stain, eosin, nissl stain, toluidine blue, silver stain, azocarmine stain, neutral red, or janus green. In some cases the cells are fixed and/or permeablized with for example methanol, ethanol, glutaraldehyde or formaldehyde prior to or during the staining procedure. In some cases, the cells are not fixed. In some cases, more than one stain is used in combination. In other cases no stain is used at all. In some cases measurement of nucleic acid content is performed using a staining procedure, for example with ethidium bromide, hematoxylin, nissl stain or any nucleic acid stain known to the art.

In some embodiments of the present invention, cells may be smeared onto a slide by standard methods well known in the art for cytological examination. In other cases, liquid based cytology (LBC) methods may be utilized. In some cases, LBC methods provide for an improved means of cytology slide preparation, more homogenous samples, increased sensitivity and specificity, and improved efficiency of handling of samples. In liquid based cytology methods, biological samples are transferred from the subject to a container or vial containing a liquid cytology preparation solution such as for example Cytyc ThinPrep, SurePath, or Monoprep or any other liquid based cytology preparation solution known in the art. Additionally, the sample may be rinsed from the collection device with liquid cytology preparation solution into the container or vial to ensure substantially quantitative transfer of the sample. The solution containing the biological sample in liquid based cytology preparation solution may then be stored and/or processed by a machine or by one skilled in the art to produce a layer of cells on a glass slide. The sample may further be stained and examined under the microscope in the same way as a conventional cytological preparation.

In some embodiments of the present invention, samples may be analyzed by immuno-histochemical staining. Immuno-histochemical staining provides for the analysis of the presence, location, and distribution of specific molecules or antigens by use of antibodies in a biological sample (e.g. cells or tissues). Antigens may be small molecules, proteins, peptides, nucleic acids or any other molecule capable of being specifically recognized by an antibody. Samples may be analyzed by immuno-histochemical methods with or without a prior fixing and/or permeabilization step. In some cases, the antigen of interest may be detected by contacting the sample with an antibody specific for the antigen and then non-specific binding may be removed by one or more washes. The specifically bound antibodies may then be detected by an antibody detection reagent such as for example a labeled secondary antibody, or a labeled avidin/streptavidin. In some cases, the antigen specific antibody may be labeled directly instead. Suitable labels for immuno-histochemistry include but are not limited to fluorophores such as fluoroscein and rhodamine, enzymes such as alkaline phosphatase and horse radish peroxidase, and radionuclides such as ³²P and ¹²⁵I. Gene product markers that may be detected by immuno-histochemical staining include but are not limited to Her2/Neu, Ras, Rho, EGFR, VEGFR, UbcH10, RET/PTC1, cytokeratin 20, calcitonin, GAL-3, thyroid peroxidase, and thyroglobulin.

VI. Analysis of Sample

In one aspect, the present invention provides methods for performing microarray gene expression analysis with low quantity and quality of polynucleotide, such as DNA or RNA. In some embodiments, the present disclosure describes methods of diagnosing, characterizing and/or monitoring a cancer by analyzing gene expression with low quantity and quality of RNA. In one embodiment, the cancer is thyroid cancer. Thyroid RNA can be obtained from fine needle aspirates (FNA). In some embodiments, gene expression profile is obtained from degraded samples with an RNA RIN value of 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0 or less. In particular embodiments, gene expression profile is obtained from a sample with an RIN of equal or less than 6, i.e. 6.0, 5.0, 4.0, 3.0, 2.0, 1.0 or less. Provided by the present invention are methods by which low quality RNA can be used to obtain meaningful gene expression results from samples containing low concentrations of nucleic acid, such as thyroid FNA samples.

Another estimate of sample usefulness is RNA yield, typically measured in nanogram to microgram amounts for gene expression assays. The most sensitive and “state of the art” apparatus used to measure nucleic acid yield in the laboratory today is the NanoDrop spectrophotometer. Like many quantitative instruments of its kind, the accuracy of a NanoDrop measurement decreases significantly with very low RNA concentration. The minimum amount of RNA necessary for input into a microarray experiment also limits the usefulness of a given sample. In some aspects, the present invention solves the low RNA concentration problem by estimating sample input using a combination of the measurements from both the NanoDrop and the Bioanalyzer instruments. Since the quality of data obtained from a gene expression study is dependent on RNA quantity, meaningful gene expression data can be generated from samples having a low or un-measurable RNA concentration as measured by NanoDrop.

The subject methods and algorithms enable: 1) gene expression analysis of samples containing low amount and/or low quality of nucleic acid; 2) a significant reduction of false positives and false negatives, 3) a determination of the underlying genetic, metabolic, or signaling pathways responsible for the resulting pathology, 4) the ability to assign a statistical probability to the accuracy of the diagnosis of genetic disorders, 5) the ability to resolve ambiguous results, and 6) the ability to distinguish between sub-types of cancer.

VI. Assay Results

The results of routine cytological or other assays may indicate a sample as negative (cancer, disease or condition free), ambiguous or suspicious (suggestive of the presence of a cancer, disease or condition), diagnostic (positive diagnosis for a cancer, disease or condition), or non diagnostic (providing inadequate information concerning the presence or absence of cancer, disease, or condition). The diagnostic results may be further classified as malignant or benign. The diagnostic results may also provide a score indicating for example, the severity or grade of a cancer, or the likelihood of an accurate diagnosis. In some cases, the diagnostic results may be indicative of a particular type of a cancer, disease, or condition, such as for example follicular adenoma, Hurthle cell adenoma, lymphocytic thyroiditis, hyperplasia, follicular carcinoma, follicular variant of papillary thyroid carcinoma, papillary carcinoma, or any of the diseases or conditions provided herein. In some cases, the diagnostic results may be indicative of a particular stage of a cancer, disease, or condition. The diagnostic results may inform a particular treatment or therapeutic intervention for the type or stage of the specific cancer disease or condition diagnosed. In some embodiments, the results of the assays performed may be entered into a database. The molecular profiling company may bill the individual, insurance provider, medical provider, or government entity for one or more of the following: assays performed, consulting services, reporting of results, database access, or data analysis. In some cases all or some steps other than molecular profiling are performed by a cytological laboratory or a medical professional.

In accordance with the foregoing, the differential copy number of a gene, genes, or markers, or a combination thereof as disclosed herein may be determined using Southern blotting and employing the sequences as identified in herein to develop probes for this purpose. Such probes may be composed of DNA or RNA or synthetic nucleotides or a combination of the above and may advantageously be comprised of a contiguous stretch of nucleotide residues matching, or complementary to, a sequence as identified in Table 1, 3, 4, 5, 6, or 8, or lists 1-45. Such probes will most usefully comprise a contiguous stretch of at least 15-200 nucleotides or more including 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 175, or 200 nucleotides or more, derived from one or more of the sequences as identified in Table 1, 3, 4, 5, 6, or 8, or lists 1-45. Thus, where a single probe binds multiple times to the transcriptome of a sample of cells that are cancerous, or are suspected of being cancerous, or predisposed to become cancerous, whereas binding of the same probe to a similar amount of transcriptome derived from the genome of otherwise non-cancerous cells of the same organ or tissue results in observably more or less binding, this is indicative of differential expression of a gene, multiple genes, markers, or miRNAs comprising, or corresponding to, the sequences identified in Table 1, 3, 4, 5, 6, or 8, or lists 1-45 from which the probe sequenced was derived.

In employing the methods of the invention, it should be borne in mind that gene or marker copy number indicative of a cancerous state need not be characteristic of every cell found to be cancerous. Thus, the methods disclosed herein are useful for detecting the presence of a cancerous condition within a tissue where less than all cells exhibit the complete pattern of alternative gene copy number. For example, a set of selected genes or markers, comprising sequences homologous under stringent conditions, or at least 90%, preferably 95%, identical to at least one of the sequences as identified in Table 1, 3, 4, 5, 6, or 8, or lists 1-45, may be found, using appropriate probes, either DNA or RNA, to be present in as little as 60% of cells derived from a sample of tumorous, or malignant, tissue while being absent from as much as 60% of cells derived from corresponding non-cancerous, or otherwise normal, tissue (and thus being present in as much as 40% of such normal tissue cells). In one embodiment, such pattern is found to be present in at least 70% of cells drawn from a cancerous tissue and absent from at least 70% of a corresponding normal, non-cancerous, tissue sample. In another embodiment, such pattern is found to be present in at least 80% of cells drawn from a cancerous tissue and absent from at least 80% of a corresponding normal, non-cancerous, tissue sample. In another embodiment, such pattern is found to be present in at least 90% of cells drawn from a cancerous tissue and absent from at least 90% of a corresponding normal, non-cancerous, tissue sample. In another embodiment, such pattern is found to be present in at least 100% of cells drawn from a cancerous tissue and absent from at least 100% of a corresponding normal, non-cancerous, tissue sample, although the latter embodiment may represent a rare occurrence.

VII. Molecular Profiling

Cytological assays mark the current diagnostic standard for many types of suspected tumors including for example thyroid tumors or nodules. In some embodiments of the present invention, samples that assay as negative, indeterminate, diagnostic, or non diagnostic may be subjected to subsequent assays to obtain more information. In the present invention, these subsequent assays comprise the steps of molecular profiling of genomic DNA, gene expression product levels or gene expression product alternative splicing. In some embodiments of the present invention, molecular profiling means the determination of the number (e.g. copy number) and/or type of genomic DNA in a biological sample. In some cases, the number and/or type may further be compared to a control sample or a sample considered normal. In some embodiment, genomic DNA can be analyzed for copy number variation, such as an increase (amplification) or decrease in copy number, or variants, such as insertions, deletions, truncations and the like. In one embodiment, deletions in the iodotyrosine deiodinase gene (IYD) can be used to detect thyroid cancer. IYD mutations have previously been shown to be involved in hypothyroidism (Moreno et al. (2008) N Engl. J Med 358:1811-8). In the present invention, a deletion in the IYD gene was shown to be involved in thyroid cancer. In some embodiments, the IYD deletion is a 50 kD deletion. The IYD gene sequence can be found, for example, in Genbank under accession number NM_(—)203395. Molecular profiling may be performed on the same sample, a portion of the same sample, or a new sample may be acquired using any of the methods described herein. The molecular profiling company may request additional sample by directly contacting the individual or through an intermediary such as a physician, third party testing center or laboratory, or a medical professional. In some cases, samples are assayed using methods and compositions of the molecular profiling business in combination with some or all cytological staining or other diagnostic methods. In other cases, samples are directly assayed using the methods and compositions of the molecular profiling business without the previous use of routine cytological staining or other diagnostic methods. In some cases the results of molecular profiling alone or in combination with cytology or other assays may enable those skilled in the art to diagnose or suggest treatment for the subject. In some cases, molecular profiling may be used alone or in combination with cytology to monitor tumors or suspected tumors over time for malignant changes.

The molecular profiling methods of the present invention provide for extracting and analyzing protein or nucleic acid (RNA or DNA) from one or more biological samples from a subject. In some cases, nucleic acid is extracted from the entire sample obtained. In other cases, nucleic acid is extracted from a portion of the sample obtained. In some cases, the portion of the sample not subjected to nucleic acid extraction may be analyzed by cytological examination or immuno-histochemistry. Methods for RNA or DNA extraction from biological samples are well known in the art and include for example the use of a commercial kit, such as the Qiagen DNeasy Blood and Tissue Kit, or the Qiagen EZ1 RNA Universal Tissue Kit.

(i) Tissue-Type Fingerprinting

In many cases, biological samples such as those provided by the methods of the present invention of may contain several cell types or tissues, including but not limited to thyroid follicular cells, thyroid medullary cells, blood cells (RBCs, WBCs, platelets), smooth muscle cells, ducts, duct cells, basement membrane, lumen, lobules, fatty tissue, skin cells, epithelial cells, and infiltrating macrophages and lymphocytes. In the case of thyroid samples, diagnostic classification of the biological samples may involve for example primarily follicular cells (for cancers derived from the follicular cell such as papillary carcinoma, follicular carcinoma, and anaplastic thyroid carcinoma) and medullary cells (for medullary cancer). The diagnosis of indeterminate biological samples from thyroid biopsies in some cases concerns the distinction of follicular adenoma vs. follicular carcinoma. The molecular profiling signal of a follicular cell for example may thus be diluted out and possibly confounded by other cell types present in the sample. Similarly diagnosis of biological samples from other tissues or organs often involves diagnosing one or more cell types among the many that may be present in the sample.

In some embodiments, the methods of the present invention provide for an upfront method of determining the cellular make-up of a particular biological sample so that the resulting genomic signatures can be calibrated against the dilution effect due to the presence of other cell and/or tissue types. In one aspect, this upfront method is an algorithm that uses a combination of known cell and/or tissue specific gene expression patterns as an upfront mini-classifier for each component of the sample. This algorithm utilizes this molecular fingerprint to pre-classify the samples according to their composition and then apply a correction/normalization factor. This data may in some cases then feed in to a final classification algorithm which would incorporate that information to aid in the final diagnosis.

(ii) Genomic Analysis

In some embodiments, genomic sequence analysis, or genotyping, may be performed on the sample. This genotyping may take the form of mutational analysis such as single nucleotide polymorphism (SNP) analysis, insertion deletion polymorphism (InDel) analysis, variable number of tandem repeat (VNTR) analysis, copy number variation (CNV) analysis (alternatively referred to as copy number polymorphism) or partial or whole genome sequencing. Methods for performing genomic analyses are known to the art and may include high throughput sequencing such as but not limited to those methods described in U.S. Pat. Nos. 7,335,762; 7,323,305; 7,264,929; 7,244,559; 7,211,390; 7,361,488; 7,300,788; and 7,280,922. Methods for performing genomic analyses may also include microarray methods as described hereinafter. FIG. 1 graphically illustrates a method for performing genomic analysis of samples of the present invention using a microarray.

In some cases, genomic analysis may be performed in combination with any of the other methods herein. For example, a sample may be obtained, tested for adequacy, and divided into aliquots. One or more aliquots may then be used for cytological analysis of the present invention, one or more may be used for gene expression profiling methods of the present invention, and one or more may be used for genomic analysis. It is further understood the present invention anticipates that one skilled in the art may wish to perform other analyses on the biological sample that are not explicitly provided herein.

In some embodiments, molecular profiling may also include but is not limited to assays of the present disclosure including assays for one or more of the following: protein expression products, RNA expression products, RNA expression product levels, RNA expression product splice variants, or DNA polymorphisms (such as copy number variations) of the genes, DNA markers, or DNA regions provided in Table 1, 3, 4, 5, 6, or 8, or lists 1-45. In some cases, the methods of the present invention provide for improved cancer diagnostics by molecular profiling of about 1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 15; 20; 25; 30; 35; 40; 45; 50; 60; 70; 80; 90; 100; 120; 140; 160; 180; 200; 240; 280; 300; 350; 400; 450; 500; 600; 700; 800; 1000; 1500; 2000; 2500; 3000; 3500; 4000; 5000; 7500; 10,000; 15,000; 20,000; 30,000; 45,000; 50,000; 60,000; 100,000; 200,000; 400,000; 600,000; 1 million; 1.5 million; 2 million or more genomic DNA markers.

In one embodiment, molecular profiling involves microarray hybridization that is performed to determine the presence or absence of DNA polymorphisms for one or more genes selected from the group consisting of Table 1, 3, 4, 5, 6, or 8, or lists 1-45. In some embodiments, DNA polymorphisms are determined for one or more genes involved in one or more of the following metabolic or signaling pathways: thyroid hormone production and/or release, protein kinase signaling pathways, lipid kinase signaling pathways, and cyclins. In some cases, the methods of the present invention provide for analysis of DNA polymorphisms of at least one gene of 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, or 15 or more different metabolic or signaling pathways.

In some embodiments of the present invention, molecular profiling includes the step of binding the sample or a portion of the sample to one or more probes of the present invention. Suitable probes bind to components of the sample, i.e. gene products, that are to be measured and include but are not limited to antibodies or antibody fragments, aptamers, nucleic acids, and oligonucleotides. The binding of the sample to the probes of the present invention represents a transformation of matter from sample to sample bound to one or more probes.

(iii) Expression Product Profiling

Gene expression profiling is the measurement of the activity (the expression) of thousands of genes at once, to create a global picture of cellular function. These profiles can, for example, distinguish between cells that are actively dividing, or show how the cells react to a particular treatment. Many experiments of this sort measure an entire genome simultaneously, that is, every gene present in a particular cell. Microarray technology measures the relative activity of previously identified target genes. Sequence based techniques, like serial analysis of gene expression (SAGE, SuperSAGE) are also used for gene expression profiling. SuperSAGE is especially accurate and can measure any active gene, not just a predefined set. In an RNA, mRNA or gene expression profiling microarray, the expression levels of thousands of genes are simultaneously monitored to study the effects of certain treatments, diseases, and developmental stages on gene expression. For example, microarray-based gene expression profiling can be used to characterize gene signatures of a genetic disorder disclosed herein, or different cancer types, subtypes of a cancer, and/or cancer stages.

Expression profiling experiments often involve measuring the relative amount of gene expression products, such as mRNA, expressed in two or more experimental conditions. This is because altered levels of a specific sequence of a gene expression product suggest a changed need for the protein coded for by the gene expression product, perhaps indicating a homeostatic response or a pathological condition. For example, if breast cancer cells express higher levels of mRNA associated with a particular transmembrane receptor than normal cells do, it might be that this receptor plays a role in breast cancer. One aspect of the present invention encompasses gene expression profiling as part of an important diagnostic test for genetic disorders and cancers, particularly, thyroid cancer.

In some embodiments, RNA samples with RIN are typically not used for multi-gene microarray analysis, and may instead be used only for single-gene RT-PCR and/or TaqMan assays. Microarray, RT-PCR and TaqMan assays are standard molecular techniques well known in the relevant art. TaqMan probe-based assays are widely used in real-time PCR including gene expression assays, DNA quantification and SNP genotyping.

In one embodiment, gene expression products related to cancer that are known to the art are profiled. Such gene expression products have been described and include but are not limited to the gene expression products detailed in U.S. Pat. Nos. 7,358,061; 7,319,011; 5,965,360; 6,436,642; and US patent applications 2003/0186248, 2005/0042222, 2003/0190602, 2005/0048533, 2005/0266443, 2006/0035244, 2006/083744, 2006/0088851, 2006/0105360, 2006/0127907, 2007/0020657, 2007/0037186, 2007/0065833, 2007/0161004, 2007/0238119, and 2008/0044824.

It is further anticipated that other gene expression products related to cancer may become known, and that the methods and compositions described herein may include such newly discovered gene expression products.

In some embodiments of the present invention gene expression products are analyzed alternatively or additionally for characteristics other than expression level. For example, gene products may be analyzed for alternative splicing. Alternative splicing, also referred to as alternative exon usage, is the RNA splicing variation mechanism wherein the exons of a primary gene transcript, the pre-mRNA, are separated and reconnected (i.e. spliced) so as to produce alternative mRNA molecules from the same gene. In some cases, these linear combinations then undergo the process of translation where a specific and unique sequence of amino acids is specified by each of the alternative mRNA molecules from the same gene resulting in protein isoforms. Alternative splicing may include incorporating different exons or different sets of exons, retaining certain introns, or using utilizing alternate splice donor and acceptor sites.

In some cases, markers or sets of markers may be identified that exhibit alternative splicing that is diagnostic for benign, malignant or normal samples. Additionally, alternative splicing markers may further provide a diagnosis for the specific type of thyroid cancer (e.g. papillary, follicular, medullary, or anaplastic). Alternative splicing markers diagnostic for malignancy known to the art include those listed in U.S. Pat. No. 6,436,642.

In some cases expression of RNA expression products that do not encode for proteins such as miRNAs, and siRNAs may be assayed by the methods of the present invention. Differential expression of these RNA expression products may be indicative of benign, malignant or normal samples. Differential expression of these RNA expression products may further be indicative of the subtype of the benign sample (e.g. FA, NHP, LCT, BN, CN, HA) or malignant sample (e.g. FC, PTC, FVPTC, ATC, MTC). In some cases, differential expression of miRNAs, siRNAs, alternative splice RNA isoforms, mRNAs or any combination thereof may be assayed by the methods of the present invention.

In some embodiments, the current invention provides 16 panels of biomarkers, each panel being required to characterize, rule out, and diagnose pathology within the thyroid. The sixteen panels are:

1 Normal Thyroid (NML) 2 Lymphocytic, Autoimmune Thyroiditis (LCT) 3 Nodular Hyperplasia (NHP) 4 Follicular Thyroid Adenoma (FA) 5 Hurthle Cell Thyroid Adenoma (HC) 6 Parathyroid (non thyroid tissue) 7 Anaplastic Thyroid Carcinoma (ATC) 8 Follicular Thyroid Carcinoma (FC) 9 Hurthle Cell Thyroid Carcinoma (HC) 10 Papillary Thyroid Carcinoma (PTC) 11 Follicular Variant of Papillary Carcinoma (FVPTC) 12 Medullary Thyroid Carcinoma (MTC) 13 Renal Carcinoma metastasis to the Thyroid 14 Melanoma metastasis to the Thyroid 15 B cell Lymphoma metastasis to the Thyroid 16 Breast Carcinoma metastasis to the Thyroid

Each panel includes a set of biomarkers required to characterize, rule out, and diagnose a given pathology within the thyroid. Panels 1-6 describe benign pathology. Panels 7-16 describe malignant pathology.

The biological nature of the thyroid and each pathology found within it, suggests that there is redundancy between the plurality of biomarkers in one panel versus the plurality of biomarkers in another panel. Mirroring each pathology subtype, each diagnostic panel is heterogeneous and semi-redundant with the biomarkers in another panel. Heterogeneity and redundancy reflect the biology of the tissues sampled in a given FNA and the differences in gene expression that characterize each pathology subtype from one another.

In one aspect, the diagnostic value of the present invention lies in the comparison of i) one or more markers in one panel, versus ii) one or more markers in each additional panel. The utility of the invention is its higher diagnostic accuracy in FNA than presently possible by any other means.

In some embodiments, the biomarkers within each panel are interchangeable (modular). The plurality of biomarkers in all panels can be substituted, increased, reduced, or improved to accommodate the definition of new pathologic subtypes (e.g. new case reports of metastasis to the thyroid from other organs). The current invention describes the plurality of markers that define each of sixteen heterogeneous, semi-redundant, and distinct pathologies found in the thyroid. All sixteen panels are required to arrive at an accurate diagnosis, and any given panel alone does not have sufficient power to make a true diagnostic determination. In some embodiments, the biomarkers in each panel are interchanged with a suitable combination of biomarkers, such that the plurality of biomarkers in each panel still defines a given pathology subtype within the context of examining the plurality of biomarkers that define all other pathology subtypes. In some embodiments, biomarkers can be combined to create larger biomarkers. In some embodiments, genomic regions can be combined to create larger genomic regions. For example, distinct genomic regions having sizes of 100 bp, 2500 bp and 3000 bp can co-map to a given gene, and can be used to create a single biomarker from the combination of the three to span up to 5600 bp.

Methods and compositions of the invention can have genes selected from 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 or 16 or more biomarker panels and can have from 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50 or more gene expression products from each biomarker panel, in any combination. In some embodiments, the set of genes combined give a specificity or sensitivity of greater than 70%, 75%, 80%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 99.5%, or a positive predictive value or negative predictive value of at least 95%, 95.5%, 96%, 96.5%, 97%, 97.5%, 98%, 98.5%, 99%, 99.5% or more.

(1) In Vitro Methods of Determining Expression Product Levels

The general methods for determining gene expression product levels are known to the art and may include but are not limited to one or more of the following: additional cytological assays, assays for specific proteins or enzyme activities, assays for specific expression products including protein or RNA or specific RNA splice variants, in situ hybridization, whole or partial genome expression analysis, microarray hybridization assays, SAGE, enzyme linked immuno-absorbance assays, mass-spectrometry, immuno-histochemistry, or blotting. Gene expression product levels may be normalized to an internal standard such as total mRNA or the expression level of a particular gene including but not limited to glyceraldehyde 3 phosphate dehydrogenase, or tublin.

In some embodiments of the present invention, gene expression product markers and alternative splicing markers may be determined by microarray analysis using, for example, Affymetrix arrays, cDNA microarrays, oligonucleotide microarrays, spotted microarrays, or other microarray products from Biorad, Agilent, or Eppendorf. Microarrays provide particular advantages because they may contain a large number of genes or alternative splice variants that may be assayed in a single experiment. In some cases, the microarray device may contain the entire human genome or transcriptome or a substantial fraction thereof allowing a comprehensive evaluation of gene expression patterns, genomic sequence, or alternative splicing. Markers may be found using standard molecular biology and microarray analysis techniques as described in Sambrook Molecular Cloning a Laboratory Manual 2001 and Baldi, P., and Hatfield, W. G., DNA Microarrays and Gene Expression 2002.

Microarray analysis begins with extracting and purifying nucleic acid from a biological sample, (e.g. a biopsy or fine needle aspirate) using methods known to the art. For expression and alternative splicing analysis it may be advantageous to extract and/or purify RNA from DNA. It may further be advantageous to extract and/or purify mRNA from other forms of RNA such as tRNA and rRNA.

Purified nucleic acid may further be labeled with a fluorescent, radionuclide, or chemical label such as biotin or digoxin for example by reverse transcription, PCR, ligation, chemical reaction or other techniques. The labeling can be direct or indirect which may further require a coupling stage. The coupling stage can occur before hybridization, for example, using aminoallyl-UTP and NHS amino-reactive dyes (like cyanine dyes) or after, for example, using biotin and labelled streptavidin. The modified nucleotides (e.g. at a 1 aaUTP: 4 TTP ratio) are added enzymatically at a lower rate compared to normal nucleotides, typically resulting in 1 every 60 bases (measured with a spectrophotometer). The aaDNA may then be purified with, for example, a column or a diafiltration device. The aminoallyl group is an amine group on a long linker attached to the nucleobase, which reacts with a reactive label (e.g. a fluorescent dye).

The labeled samples may then be mixed with a hybridization solution which may contain SDS, SSC, dextran sulfate, a blocking agent (such as COT1 DNA, salmon sperm DNA, calf thymum DNA, PolyA or PolyT), Denhardt's solution, formamine, or a combination thereof.

A hybridization probe is a fragment of DNA or RNA of variable length, which is used to detect in DNA or RNA samples the presence of nucleotide sequences (the DNA target) that are complementary to the sequence in the probe. The probe thereby hybridizes to single-stranded nucleic acid (DNA or RNA) whose base sequence allows probe-target base pairing due to complementarity between the probe and target. The labeled probe is first denatured (by heating or under alkaline conditions) into single DNA strands and then hybridized to the target DNA.

To detect hybridization of the probe to its target sequence, the probe is tagged (or labeled) with a molecular marker; commonly used markers are ³²P or Digoxigenin, which is non-radioactive antibody-based marker. DNA sequences or RNA transcripts that have moderate to high sequence similarity to the probe are then detected by visualizing the hybridized probe via autoradiography or other imaging techniques. Detection of sequences with moderate or high similarity depends on how stringent the hybridization conditions were applied high stringency, such as high hybridization temperature and low salt in hybridization buffers, permits only hybridization between nucleic acid sequences that are highly similar, whereas low stringency, such as lower temperature and high salt, allows hybridization when the sequences are less similar. Hybridization probes used in DNA microarrays refer to DNA covalently attached to an inert surface, such as coated glass slides or gene chips, and to which a mobile cDNA target is hybridized.

This mix may then be denatured by heat or chemical means and added to a port in a microarray. The holes may then be sealed and the microarray hybridized, for example, in a hybridization oven, where the microarray is mixed by rotation, or in a mixer. After an overnight hybridization, non specific binding may be washed off (e.g. with SDS and SSC). The microarray may then be dried and scanned in a special machine where a laser excites the dye and a detector measures its emission. The image may be overlaid with a template grid and the intensities of the features (several pixels make a feature) may be quantified.

Various kits can be used for the amplification of nucleic acid and probe generation of the subject methods. Examples of kit that can be used in the present invention include but are not limited to Nugen WT-Ovation FFPE kit, cDNA amplification kit with Nugen Exon Module and Frag/Label module. The NuGEN WT-Ovation™ FFPE System V2 is a whole transcriptome amplification system that enables conducting global gene expression analysis on the vast archives of small and degraded RNA derived from FFPE samples. The system is comprised of reagents and a protocol required for amplification of as little as 50 ng of total FFPE RNA. The protocol can be used for qPCR, sample archiving, fragmentation, and labeling. The amplified cDNA can be fragmented and labeled in less than two hours for GeneChip® 3′ expression array analysis using NuGEN's FL-Ovation™ cDNA Biotin Module V2. For analysis using Affymetrix GeneChip® Exon and Gene ST arrays, the amplified cDNA can be used with the WT-Ovation Exon Module, then fragmented and labeled using the FL-Ovation™ cDNA Biotin Module V2. For analysis on Agilent arrays, the amplified cDNA can be fragmented and labeled using NuGEN's FL-Ovation™ cDNA Fluorescent Module. More information on Nugen WT-Ovation FFPE kit can be obtained at http://www.nugeninc.com/nugen/index.cfm/products/amplification-systems/wt-ovation-ffpe/.

In some embodiments, Ambion WT-expression kit can be used. Ambion WT-expression kit allows amplification of total RNA directly without a separate ribosomal RNA (rRNA) depletion step. With the Ambion® WT Expression Kit, samples as small as 50 ng of total RNA can be analyzed on Affymetrix® GeneChip® Human, Mouse, and Rat Exon and Gene 1.0 ST Arrays. In addition to the lower input RNA requirement and high concordance between the Affymetrix® method and TaqMan® real-time PCR data, the Ambion® WT Expression Kit provides a significant increase in sensitivity. For example, a greater number of probe sets detected above background can be obtained at the exon level with the Ambion® WT Expression Kit as a result of an increased signal-to-noise ratio. Ambion WT-expression kit may be used in combination with additional Affymetrix labeling kit.

In some embodiments, AmpTec Trinucleotide Nano mRNA Amplification kit (6299-A15) can be used in the subject methods. The ExpressArt® TRinucleotide mRNA amplification Nano kit is suitable for a wide range, from 1 ng to 700 ng of input total RNA. According to the amount of input total RNA and the required yields of aRNA, it can be used for 1-round (input >300 ng total RNA) or 2-rounds (minimal input amount 1 ng total RNA), with aRNA yields in the range of >10 μg. AmpTec's proprietary TRinucleotide priming technology results in preferential amplification of mRNAs (independent of the universal eukaryotic 3′-poly(A)-sequence), combined with selection against rRNAs. More information on AmpTec Trinucleotide Nano mRNA Amplification kit can be obtained at http://www.amp-tec.com/products.htm. This kit can be used in combination with cDNA conversion kit and Affymetrix labeling kit.

The raw data may then be normalized, for example, by subtracting the background intensity and then dividing the intensities making either the total intensity of the features on each channel equal or the intensities of a reference gene and then the t-value for all the intensities may be calculated. More sophisticated methods, include z-ratio, loess and lowess regression and RMA (robust multichip analysis) for Affymetrix chips.

(2) In Vivo Methods of Determining Gene Expression Product Levels

It is further anticipated that the methods and compositions of the present invention may be used to determine gene expression product levels in an individual without first obtaining a sample. For example, gene expression product levels may be determined in vivo, that is in the individual. Methods for determining gene expression product levels in vivo are known to the art and include imaging techniques such as CAT, MRI; NMR; PET; and optical, fluorescence, or biophotonic imaging of protein or RNA levels using antibodies or molecular beacons. Such methods are described in US 2008/0044824, US 2008/0131892, herein incorporated by reference. Additional methods for in vivo molecular profiling are contemplated to be within the scope of the present invention.

In some embodiments of the present invention, molecular profiling includes the step of binding the sample or a portion of the sample to one or more probes of the present invention. Suitable probes bind to components of the sample, i.e. gene products, that are to be measured and include but are not limited to antibodies or antibody fragments, aptamers, nucleic acids, and oligonucleotides. The binding of the sample to the probes of the present invention represents a transformation of matter from sample to sample bound to one or more probes. The method of diagnosing cancer based on molecular profiling further comprises the steps of detecting gene expression products (i.e. mRNA or protein) and levels of the sample, comparing it to an amount in a normal control sample to determine the differential gene expression product level between the sample and the control; and classifying the test sample by inputting one or more differential gene expression product levels to a trained algorithm of the present invention; validating the sample classification using the selection and classification algorithms of the present invention; and identifying the sample as positive for a genetic disorder or a type of cancer.

(iii) Comparison of Sample to Normal

The results of the molecular profiling performed on the sample provided by the individual (test sample) may be compared to a biological sample that is known or suspected to be normal. A normal sample is that which is or is expected to be free of any cancer, disease, or condition, or a sample that would test negative for any cancer disease or condition in the molecular profiling assay. The normal sample may be from a different individual from the individual being tested, or from the same individual. The normal sample may be assayed at the same time, or at a different time from the test sample. In some cases, the normal sample may be obtained from a buccal swab.

The results of an assay on the test sample may be compared to the results of the same assay on a normal sample. In some cases the results of the assay on the normal sample are from a database, or a reference. In some cases, the results of the assay on the normal sample are a known or generally accepted value by those skilled in the art. In some cases the comparison is qualitative. In other cases the comparison is quantitative. In some cases, qualitative or quantitative comparisons may involve but are not limited to one or more of the following: comparing fluorescence values, spot intensities, absorbance values, chemiluminescent signals, histograms, critical threshold values, statistical significance values, gene product expression levels, gene product expression level changes, alternative exon usage, changes in alternative exon usage, DNA polymorphisms, copy number variations, indications of the presence or absence of one or more DNA markers or regions, or nucleic acid sequences.

(iv) Evaluation of Results

In some embodiments, the molecular profiling results are evaluated using methods known to the art for correlating DNA polymorphisms with specific phenotypes such as malignancy, the type of malignancy (e.g. follicular carcinoma), benignancy, or normalcy (e.g. disease or condition free). In some cases, a specified statistical confidence level may be determined in order to provide a diagnostic confidence level. For example, it may be determined that a confidence level of greater than 90% may be a useful predictor of malignancy, type of malignancy, normalcy, or benignancy. In other embodiments, more stringent or looser confidence levels may be chosen. For example, a confidence level of approximately 70%, 75%, 80%, 85%, 90%, 95%, 97.5%, 99%, 99.5%, or 99.9% may be chosen as a useful phenotypic predictor. The confidence level provided may in some cases be related to the quality of the sample, the quality of the data, the quality of the analysis, the specific methods used, and the number of genes, markers or genomic regions analyzed. The specified confidence level for providing a diagnosis may be chosen on the basis of the expected number of false positives or false negatives and/or cost. Methods for choosing parameters for achieving a specified confidence level or for identifying markers with diagnostic power include but are not limited to Receiver Operator Curve analysis (ROC), binormal ROC, principal component analysis, partial least squares analysis, singular value decomposition, least absolute shrinkage and selection operator analysis, least angle regression, and the threshold gradient directed regularization method.

(v) Data Analysis

Raw data, such as for example microarray data, may in some cases be improved through the application of algorithms designed to normalize and or improve the reliability of the data. In some embodiments of the present invention the data analysis requires a computer or other device, machine or apparatus for application of the various algorithms described herein due to the large number of individual data points that are processed. A “machine learning algorithm” refers to a computational-based prediction methodology, also known to persons skilled in the art as a “classifier”, employed for characterizing a gene expression profile. The signals corresponding to certain expression levels, which are obtained by, e.g., microarray-based hybridization assays, are typically subjected to the algorithm in order to classify the expression profile. Supervised learning generally involves “training” a classifier to recognize the distinctions among classes and then “testing” the accuracy of the classifier on an independent test set. For new, unknown samples the classifier can be used to predict the class in which the samples belong.

In some cases, the robust multi-array Average (RMA) method may be used to normalize the raw data. The RMA method begins by computing background-corrected intensities for each matched cell on a number of microarrays. The background corrected values are restricted to positive values as described by Irizarry et al. Biostatistics 2003 Apr. 4 (2): 249-64. After background correction, the base-2 logarithm of each background corrected matched-cell intensity is then obtained. The back-ground corrected, log-transformed, matched intensity on each microarray is then normalized using the quantile normalization method in which for each input array and each probe expression value, the array percentile probe value is replaced with the average of all array percentile points, this method is more completely described by Bolstad et al. Bioinformatics 2003. Following quantile normalization, the normalized data may then be fit to a linear model to obtain an expression measure for each probe on each microarray. Tukey's median polish algorithm (Tukey, J. W., Exploratory Data Analysis. 1977) may then be used to determine the log-scale expression level for the normalized probe set data.

Data may further be filtered to remove data that may be considered suspect. In some embodiments, data deriving from microarray probes that have fewer than about 4, 5, 6, 7 or 8 guanosine+cytosine nucleotides may be considered to be unreliable due to their aberrant hybridization propensity or secondary structure issues. Similarly, data deriving from microarray probes that have more than about 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, or 22 guanosine+cytosine nucleotides may be considered unreliable due to their aberrant hybridization propensity or secondary structure issues.

In some cases, unreliable probe sets may be selected for exclusion from data analysis by ranking probe-set reliability against a series of reference datasets. For example, RefSeq or Ensembl (EMBL) are considered very high quality reference datasets. Data from probe sets matching RefSeq or Ensembl sequences may in some cases be specifically included in microarray analysis experiments due to their expected high reliability. Similarly data from probe-sets matching less reliable reference datasets may be excluded from further analysis, or considered on a case by case basis for inclusion. In some cases, reference datasets may be used to determine the probe-set reliability separately or together. In some cases, probe-set reliability may be ranked. For example, probes and/or probe-sets that match perfectly to all reference datasets may be ranked as most reliable (1). Furthermore, probes and/or probe-sets that match two out of three reference datasets may be ranked as next most reliable (2), probes and/or probe-sets that match one out of three reference datasets may be ranked next (3) and probes and/or probe sets that match no reference datasets may be ranked last (4). Probes and or probe-sets may then be included or excluded from analysis based on their ranking. For example, one may choose to include data from category 1, 2, 3, and 4 probe-sets; category 1, 2, and 3 probe-sets; category 1 and 2 probe-sets; or category 1 probe-sets for further analysis. In another example, probe-sets may be ranked by the number of base pair mismatches to reference dataset entries. It is understood that there are many methods understood in the art for assessing the reliability of a given probe and/or probe-set for molecular profiling and the methods of the present invention encompass any of these methods and combinations thereof.

In some embodiments of the present invention, probe-sets for a given gene or transcript cluster may be excluded from further analysis if they contain less than a minimum number of probes that pass through the previously described filter steps for GC content, reliability, variance and the like. For example in some embodiments, probe-sets for a given gene or transcript cluster may be excluded from further analysis if they contain less than about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or less than about 20 probes.

Methods of data analysis of microarray data may further include the use of a pre-classifier algorithm. For example, fine needle aspirates (FNAs) of thyroid nodules contain several cell types, including thyroid follicular cells, thyroid medullary cells, blood cells (RBCs, WBCs, platelets), smooth muscle cells and infiltrating macrophages and lymphocytes. Diagnostic classification of FNAs involves primarily follicular cells (for cancers derived from the follicular cell such as papillary carcinoma, follicular carcinoma, and anaplastic thyroid carcinoma) and medullary cells (for medullary cancer). Since medullary and anaplastic thyroid cancers are rarely present in the indeterminate class, the diagnosis of indeterminate FNAs mainly concerns the distinction of follicular adenoma versus follicular carcinoma. The molecular profiling (e.g. copy number variation or other DNA polymorphism) signal of the follicular cell is thus diluted out and possibly confounded by other cell types present in the FNA. An upfront method of determining the cellular make-up of a particular FNA may allow the resulting molecular profiling signatures to be calibrated against the dilution effect. A combination of known cell-specific gene expression patterns may be used as an upfront mini-classifier for each cell component of the FNA. An algorithm may then use this cell-specific molecular fingerprint, pre-classify the samples according to their composition and then apply a correction/normalization factor. This data/information may then be fed in to a final classification algorithm which would incorporate that information to aid in the final diagnosis of Benign or Normal versus Malignant. Thus, in some embodiments, the analysis of genomic DNA by the methods of the present invention includes analysis of gene expression patterns to thereby provide a correction/normalization factor for the cellular composition of the sample.

In some embodiments of the present invention, data from probe-sets may be excluded from analysis if they are not expressed or expressed at an undetectable level (not above background). A probe-set is judged to be expressed above background if for any group:

Integral from T0 to Infinity of the standard normal distribution<Significance (0.01)

Where: T0=Sqr(GroupSize) (T−P)/Sqr(Pvar),

Group Size=Number of CEL files in the group, T=Average of probe scores in probe-set, P=Average of Background probes averages of GC content, and Pvar=Sum of Background probe variances/(Number of probes in probe-set)̂2,

This allows including probe-sets in which the average of probe-sets in a group is greater than the average expression of background probes of similar GC content as the probe-set probes as the center of background for the probe-set and enables one to derive the probe-set dispersion from the background probe-set variance.

In some embodiments of the present invention, probe-sets that exhibit no, or low variance may be excluded from further analysis. Low-variance probe-sets are excluded from the analysis via a Chi-Square test. A probe-set is considered to be low-variance if its transformed variance is to the left of the 99 percent confidence interval of the Chi-Squared distribution with (N−1) degrees of freedom. (N−1)*Probe-set Variance/(Gene Probe-set Variance)˜Chi-Sq(N−1)

where N is the number of input CEL files, (N−1) is the degrees of freedom for the Chi-Squared distribution, and the ‘probe-set variance for the gene’ is the average of probe-set variances across the gene.

In some embodiments of the present invention, probe-sets for a given gene or transcript cluster may be excluded from further analysis if they contain less than a minimum number of probes that pass through the previously described filter steps for GC content, reliability, variance and the like. For example in some embodiments, probe-sets for a given gene or transcript cluster may be excluded from further analysis if they contain less than about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or less than about 20 probes.

Methods of data analysis of gene expression levels or of alternative splicing may further include the use of a feature selection algorithm as provided herein. In some embodiments of the present invention, feature selection is provided by use of the LIMMA software package (Smyth, G. K. (2005). Limma: linear models for microarray data. In: Bioinformatics and Computational Biology Solutions using R and Bioconductor, R. Gentleman, V. Carey, S. Dudoit, R. Irizarry, W. Huber (eds.), Springer, New York, pages 397-420).

Methods of data analysis of gene expression levels and or of alternative splicing may further include the use of a pre-classifier algorithm. For example, an algorithm may use a cell-specific molecular fingerprint to pre-classify the samples according to their composition and then apply a correction/normalization factor. This data/information may then be fed in to a final classification algorithm which would incorporate that information to aid in the final diagnosis.

Methods of data analysis of gene expression levels and or of alternative splicing may further include the use of a classifier algorithm as provided herein. In some embodiments of the present invention a support vector machine (SVM) algorithm, a random forest algorithm, or a combination thereof is provided for classification of microarray data. In some embodiments, identified markers that distinguish samples (e.g. benign vs. malignant, normal vs. malignant) or distinguish subtypes (e.g. PTC vs. FVPTC) are selected based on statistical significance. In some cases, the statistical significance selection is performed after applying a Benjamini Hochberg correction for false discovery rate (FDR).

In some cases, the classifier algorithm may be supplemented with a meta-analysis approach such as that described by Fishel and Kaufman et al. 2007 Bioinformatics 23(13): 1599-606. In some cases, the classifier algorithm may be supplemented with a meta-analysis approach such as a repeatability analysis. In some cases, the repeatability analysis selects markers that appear in at least one predictive expression product marker set.

In some cases, the results of feature selection and classification may be ranked using a Bayesian post-analysis method. For example, microarray data may be extracted, normalized, and summarized using methods known in the art such as the methods provided herein. The data may then be subjected to a feature selection step such as any feature selection methods known in the art such as the methods provided herein including but not limited to the feature selection methods provided in LIMMA. The data may then be subjected to a classification step such as any of the classification methods known in the art such as the use of any of the algorithms or methods provided herein including but not limited to the use of SVM or random forest algorithms. The results of the classifier algorithm may then be ranked by according to a posterior probability function. For example, the posterior probability function may be derived from examining known molecular profiling results, such as published results, to derive prior probabilities from type I and type II error rates of assigning a marker to a category (e.g. benign, malignant, normal, ATC, PTC, MTC, FC, FN, FA, FVPTC CN, HA, HC, LCT, NHP etc.). These error rates may be calculated based on reported sample size for each study using an estimated fold change value (e.g. 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2, 2.2, 2.4, 2.5, 3, 4, 5, 6, 7, 8, 9, 10 or more). These prior probabilities may then be combined with a molecular profiling dataset of the present invention to estimate the posterior probability of differential gene expression. Finally, the posterior probability estimates may be combined with a second dataset of the present invention to formulate the final posterior probabilities of differential expression. Additional methods for deriving and applying posterior probabilities to the analysis of microarray data are known in the art and have been described for example in Smyth, G. K. 2004 Stat. Appl. Genet. Mol. Biol. 3: Article 3. In some cases, the posterior probabilities may be used to rank the markers provided by the classifier algorithm. In some cases, markers may be ranked according to their posterior probabilities and those that pass a chosen threshold may be chosen as markers whose differential expression is indicative of or diagnostic for samples that are for example benign, malignant, normal, ATC, PTC, MTC, FC, FN, FA, FVPTC CN, HA, HC, LCT, or NHP. Illustrative threshold values include prior probabilities of 0.7, 0.75, 0.8, 0.85, 0.9, 0.925, 0.95, 0.975, 0.98, 0.985, 0.99, 0.995 or higher.

A statistical evaluation of the results of the molecular profiling may provide a quantitative value or values indicative of one or more of the following: the likelihood of diagnostic accuracy, the likelihood of cancer, disease or condition, the likelihood of a particular cancer, disease or condition, the likelihood of the success of a particular therapeutic intervention. Thus a physician, who is not likely to be trained in genetics or molecular biology, need not understand the raw data. Rather, the data is presented directly to the physician in its most useful form to guide patient care. The results of the molecular profiling can be statistically evaluated using a number of methods known to the art including, but not limited to: the students T test, the two sided T test, pearson rank sum analysis, hidden markov model analysis, analysis of q-q plots, principal component analysis, one way ANOVA, two way ANOVA, LIMMA and the like.

In some embodiments of the present invention, the use of molecular profiling alone or in combination with cytological analysis may provide a diagnosis that is between about 85% accurate and about 99% or about 100% accurate. In some cases, the molecular profiling business may through the use of molecular profiling and/or cytology provide a diagnosis of malignant, benign, or normal that is about 85%, 86%, 87%, 88%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 97.5%, 98%, 98.5%, 99%, 99.5%, 99.75%, 99.8%, 99.85%, or 99.9% accurate.

In some cases, accuracy may be determined by tracking the subject over time to determine the accuracy of the original diagnosis. In other cases, accuracy may be established in a deterministic manner or using statistical methods. For example, receiver operator characteristic (ROC) analysis may be used to determine the optimal assay parameters to achieve a specific level of accuracy, specificity, positive predictive value, negative predictive value, and/or false discovery rate. Methods for using ROC analysis in cancer diagnosis are known in the art and have been described for example in US Patent Application No. 2006/019615 herein incorporated by reference in its entirety.

In some embodiments of the present invention, polynucleotides encoding for genomic DNA markers or regions or their complement which are determined to exhibit the greatest difference in the presence or absence of one or more DNA polymorphisms or the presence or absence of one or more copy number variations between benign and normal, benign and malignant, malignant and normal, or between any one of the diseases or conditions provided herein and any other disease or condition provided herein including malignancy, benignancy, or normalcy may be chosen for use as molecular profiling reagents of the present invention. Such polynucleotides may be particularly useful by providing a wider dynamic range, greater signal to noise, improved diagnostic power, lower likelihood of false positives or false negative, or a greater statistical confidence level than current methods known and used in the art.

In other embodiments of the present invention, the use of molecular profiling alone or in combination with cytological analysis may reduce the number of samples scored as non-diagnostic by about 100%, 99%, 95%, 90%, 80%, 75%, 70%, 65%, or about 60% when compared to the use of standard cytological techniques known to the art. In some cases, the methods of the present invention may reduce the number of samples scored as intermediate or suspicious by about 100%, 99%, 98%, 97%, 95%, 90%, 85%, 80%, 75%, 70%, 65%, or about 60%, when compared to the standard cytological methods used in the art.

In some cases the results of the molecular profiling assays, are entered into a database for access by representatives or agents of the molecular profiling business, the individual, a medical provider, or insurance provider. In some cases assay results include interpretation or diagnosis by a representative, agent or consultant of the business, such as a medical professional. In other cases, a computer or algorithmic analysis of the data is provided automatically. In some cases the molecular profiling business may bill the individual, insurance provider, medical provider, researcher, or government entity for one or more of the following: molecular profiling assays performed, consulting services, data analysis, reporting of results, or database access.

In some embodiments of the present invention, the results of the molecular profiling are presented as a report on a computer screen or as a paper record. In some cases, the report may include, but is not limited to, such information as one or more of the following: the presence or absence of DNA markers or regions exhibiting polymorphisms or copy number variations, the number of genes differentially expressed, the suitability of the original sample, the number of genes showing differential alternative splicing, a diagnosis, a statistical confidence for the diagnosis, the likelihood of cancer or malignancy, and indicated therapies.

(vi) Categorization of Samples Based on Molecular Profiling Results

The results of the molecular profiling may be classified into one of the following: negative (free of a cancer, disease, or condition), diagnostic (positive diagnosis for a cancer, disease, or condition), indeterminate or suspicious (suggestive of a cancer, disease, or condition), or non diagnostic (providing inadequate information concerning the presence or absence of a cancer, disease, or condition). In some cases, a diagnostic result may further classify the type of cancer, disease or condition. In other cases, a diagnostic result may indicate a certain molecular pathway involved in the cancer disease or condition, or a certain grade or stage of a particular cancer disease or condition. In still other cases a diagnostic result may inform an appropriate therapeutic intervention, such as a specific drug regimen like a kinase inhibitor such as Gleevec or any drug known to the art, or a surgical intervention like a thyroidectomy or a hemithyroidectomy.

In some embodiments of the present invention, results are classified using a trained algorithm. Trained algorithms of the present invention include algorithms that have been developed using a reference set of known malignant, benign, and normal samples including but not limited to the samples listed in Table 2. Algorithms suitable for categorization of samples include but are not limited to k-nearest neighbor algorithms, concept vector algorithms, naive bayesian algorithms, neural network algorithms, hidden markov model algorithms, genetic algorithms, and mutual information feature selection algorithms or any combination thereof. In some cases, trained algorithms of the present invention may incorporate data other than genomic DNA polymorphism data (e.g. copy number variation data), gene expression data or alternative splicing data such as but not limited to scoring or diagnosis by cytologists or pathologists of the present invention, information provided by the pre-classifier algorithm of the present invention, or information about the medical history of the subject of the present invention.

(vii) Monitoring of Subjects or Therapeutic Interventions Via Molecular Profiling

In some embodiments, a subject may be monitored using methods and compositions of the present invention. For example, a subject may be diagnosed with cancer. This initial diagnosis may or may not involve the use of molecular profiling. The subject may be prescribed a therapeutic intervention such as for example, a thyroidectomy, a hemithyroidectomy, hormone or hormone agonist treatment, chemotherapeutic treatment, or radiation treatment. The results of the therapeutic intervention may be monitored on an ongoing basis by molecular profiling to detect the efficacy of the therapeutic intervention. In another example, a subject may be diagnosed with a benign tumor or a precancerous lesion or nodule, and the tumor, nodule, or lesion may be monitored on an ongoing basis by molecular profiling to detect any changes in the state of the tumor or lesion.

Molecular profiling may also be used to ascertain the potential efficacy of a specific therapeutic intervention prior to administering to a subject. For example, a subject may be diagnosed with cancer. Molecular profiling may indicate a change in the copy number of a region of genomic DNA known to contain or be near one or more genes involved in a thyroid disease or condition, such as for example the RAS oncogene, or the iodotyrosine deiodinase gene. A tumor sample may be obtained and cultured in vitro using methods known to the art. The application of various inhibitors of the aberrantly activated, inactivated, or dysregulated pathway, or drugs known to inhibit the activity of the pathway may then be tested against the tumor cell line for growth inhibition. Molecular profiling may also be used to monitor the effect of these inhibitors on for example down-stream targets of the implicated pathway.

(viii) Molecular Profiling as a Research Tool

In some embodiments, molecular profiling may be used as a research tool to identify new markers for diagnosis of suspected tumors; to monitor the effect of drugs or candidate drugs on biological samples such as tumor cells, cell lines, tissues, or organisms; or to uncover new pathways for oncogenesis and/or tumor suppression.

Compositions

Compositions of the present disclosure are also provided which composition comprises one or more of the following: nucleotides (e.g. DNA or RNA) corresponding to the intergenic regions, genes or a portion of the genes provided in Table 1, 3, 4, 5, 6 or 8, or lists 1-45. The nucleotides of the present invention can be at least about 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 100, 150, 200, 250, 300, 350, or about 400 or 500 nucleotides in length. In some embodiments of the present invention, the nucleotides can be natural or man-made derivatives of ribonucleic acid or deoxyribonucleic acid including but not limited to peptide nucleic acids, pyranosyl RNA, nucleosides, methylated nucleic acid, pegylated nucleic acid, cyclic nucleotides, and chemically modified nucleotides. In some of the compositions of the present invention, nucleotides of the present invention have been chemically modified to include a detectable label. In some embodiments of the present invention the biological sample has been chemically modified to include a label. In some embodiments, the compositions provided herein include nucleotides (e.g. DNA or RNA) immobilized to a solid substrate such as one or more beads, a plate, an array, a microarray, or one or more wells or spots.

A further composition of the present disclosure comprises oligonucleotides for detecting or measuring DNA polymorphisms such as copy number variations or SNPs or any of the polymorphisms provided herein of any of the genes or DNA regions provided in Table 1, 3, 4, 5, 6 or 8, or lists 1-45 or their complement. A further composition of the present disclosure comprises oligonucleotides for detecting (i.e. measuring) the expression products of polymorphic alleles of the genes provided in Table 1, 3, 4, 5, 6 or 8, or lists 1-45, or their complement. Such polymorphic alleles include but are not limited to splice site variants, single nucleotide polymorphisms, variable number repeat polymorphisms, insertions, deletions, and homologues. In some cases, the variant alleles are between about 99.9% and about 70% identical to the genes listed in Table 1, 3, 4, 5, 6 or 8, or lists 1-45, including about 99.75%, 99.5%, 99.25%, 99%, 97.5%, 95%, 92.5%, 90%, 85%, 80%, 75%, and about 70% identical. In some cases, the variant alleles differ by between about 1 nucleotide and about 500 nucleotides from the genes provided in Table 1, 3, 4, 5, 6 or 8, or lists 1-45, including about 1, 2, 3, 5, 7, 10, 15, 20, 25, 30, 35, 50, 75, 100, 150, 200, 250, 300, and about 400 nucleotides.

(Vii) Biomarker Groupings Based on Molecular Profiling

Thyroid genes can be described according to the groups 1) Benign vs. Malignant, 2) alternative gene splicing, 3) KEGG Pathways, 4) Normal Thyroid, 5) Thyroid pathology subtype, 6) Gene Ontology, and 7) Biomarkers of metastasis to the thyroid from non-thyroid organs. Methods and compositions of the invention can have genes selected from one or more of the groups listed above and/or 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50 or more subgroups from any of the groups listed above (e.g. one or more different KEGG pathway) and can have from 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50 or more gene expression products from each group, in any combination. Use of multiple genes in different pathways in the present invention can be indicative of a particular thyroid pathology. For example, this can be useful to indicate that genetic changes in different pathways can modify redundant systems of cellular regulation. In other embodiments, use of multiple genes in a single pathway in the present invention can be indicative of a particular thyroid pathology. For example, this can be useful to confirm that there is corruption in a particular pathway. In some embodiments, the set of genes combined give a specificity or sensitivity of greater than 70%, 75%, 80%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 99.5%, or a positive predictive value or negative predictive value of at least 95%, 95.5%, 96%, 96.5%, 97%, 97.5%, 98%, 98.5%, 99%, 99.5% or more.

In some embodiments, the extracellular matrix, adherens, focal adhesion, and tight junction genes are used as biomarkers of thyroid cancer. In some embodiments, the signaling pathway is selected from one of the following three pathways: adherens pathway, focal adhesion pathway, and tight junction pathway. In some embodiments, at least one gene is selected from one of the 3 pathways. In some embodiments, at least one gene is selected from each one of the three pathways. In some embodiments, at least one gene is selected from two of the three pathways. In some embodiments, at least one gene that is involved in all three pathways is selected. In one example, a set of genes that is involved in adherens pathway, focal adhesion pathway, and tight junction pathway is selected as the markers for diagnosis of a cancer such as thyroid cancer.

The follicular cells that line thyroid follicles are highly polarized and organized in structure, requiring distinct roles of their luminal and apical cell membranes. In some embodiments, cytoskeleton, plasma membrane, and extracellular space genes are used as biomarkers of thyroid cancer. In some embodiments, genes that overlap all four pathways, i.e. ECM, focal adhesion, adherens, and tight junction pathways, are used as biomarkers of thyroid cancer. In one example, the present invention provides the Benign vs. malignant group as a thyroid classification gene list. This list has been grouped according to KEGG pathways, gene ontology and chromosomal localization (see for example, Tables 1, 3, 4, 5, 6, or 8 or lists 1-45). KEGG pathways are further described in Table 1.

TABLE 1 Genes involved in the KEGG Pathways Total Genes in KEGG Pathway Pathway Genes Renal Cell Carcinoma 4 MET, RAF1, RAP1A, NRAS Long-term potentiation 4 RAF1, RAP1A, NRAS, PPP3R2 Olfactory Transduction 10 OR2T3, CLCA4, OR10J5, OR2A42, OR2A5, OR2T34, OR51A2, OR51A4, OR5A2, OR5B21 B-cell Receptor 4 FOS, RAF1, NRAS, PPP3R2 Colorectal Cancer 4 FOS, MET, MSH3, RAF1 Notch Signaling 3 CTBP2, DTX4, NOTCH2 T-cell Receptor Signaling 4 FOS, RAF1, NRAS, PPP3R2 Thyroid Cancer 2 PAX8, NRAS Melanoma 3 MET, RAF1, NRAS Chronic myeloid leukemia 3 RAF1, CTBP2, NRAS VEGF signaling 3 RAF1, NRAS, PPP3R2 Axon guidance 4 ABLIM1, MET, NRAS, PPP3R2 Endocytosis 5 MET, NEDD4L, PSD4, IQSEC1, IQSEC3

In some embodiments, markers for diagnosis of a cancer such as thyroid cancer are selected for examination from the following lists of markers:

Thyroid surgical pathology subtypes are listed below:

(i) List 1: FC subtype, n=3: PAX8, PSD4, IYD

(ii) List 2: FVPTC subtype, n=15: ARHGEF5, LOC728377, OR2A20P, OR2A5, OR2A9P, OR2A42, ZNF486, ZNF626, ZNF826, RMST, RMST, OR2A20P, OR2A5, OR2A9P, p12-33192661-33198630 [IS THIS CORRECT?]

(iii) List 3: PTC subtype, n=9: AK129935, BC043197, DUSP22, ENST00000384854, ENST00000398221, LOC645433, LRRK2, RMST, XM_(—)001132965

(vi) List 4: NHP subtype, n=2: OR2T3, OR2T34

Dominant gene ontology thyroid biomarkers are listed below:

List 5: multicellular organismal process, n=73: ABLIM1 AFAP1L2 AGTR1 CD9 CDK5RAP2 DSCAML1 DUSP22 DYSF EPS8 FOS GRIN3A HMGA2 MET MICALCL MLLT3 MSH3 NEDD4L OPRM1 OR2T3 PARK2 PAX8 PPAP2B TGFBR3 B4GALT1 Clorf68 CNN3 CSDE1 DAD1 EDN3 EMP1 EPOR FAT3 FKBP1A FOXE1 HLA-DOA HNF4A HOXC10 HOXC11 HOXC12 HOXC13 HOXC4 HOXC5 HOXC6 HOXC8 HOXC9 KRT71 LCE1C LCE1E LCE3B LCE3C LCE3E LCE6A MMP26 MYT1 NOBOX NOTCH2 NOTCH2NL NPNT NRAS OR10J5 OR2A42 OR2A5 OR2T34 OR51A2 OR51A4 OR5A2 OR5B21 OR6J1 PLSCR1 SCIN SLC5A3 TMC1 ZFAND5

List 6: multicellular organismal development, n=53: ABLIM1 AGTR1 CD9 CDK5RAP2 DSCAML1 DUSP22 FOS GRIN3A HMGA2 MET MICALCL MLLT3 MSH3 PARK2 PAX8 PPAP2B TGFBR3 B4GALT1 Clorf68 CSDE1 DAD1 EDN3 EMP1 EPOR FAT3 FKBP1A FOXE1 HLA-DOA HOXC10 HOXC11 HOXC12 HOXC13 HOXC4 HOXC5 HOXC6 HOXC8 HOXC9 KRT71 LCE1C LCE1E LCE3B LCE3C LCE3E LCE6A MYT1 NOBOX NOTCH2 NOTCH2NL NPNT NRAS SCIN SLC5A3 ZFAND5

List 7: anteriorposterior pattern formation, n=9: MLLT3 HOXC10 HOXC11 HOXC13 HOXC4 HOXC5 HOXC6 HOXC8 HOXC9

List 8: epidermis development, n=11: Clorf68 EMP1 FOXE1 HOXC13 KRT71 LCE1C LCE1E LCE3B LCE3C LCE3E LCE6A

List 9: regionalization, n=10: DSCAML1 MLLT3 HOXC10 HOXC11 HOXC13 HOXC4 HOXC5 HOXC6 HOXC8 HOXC9

List 10: ectoderm development, n=11: FOXE1 HOXC13 KRT71 LCE1C LCE1E LCE3B LCE3C LCE3E LCE6A

List 11: keratinization, n=6: LCE1C LCE1E LCE3B LCE3C LCE3E LCE6A

List 12: developmental process, n=58: ABLIM1 AGTR1 CD9 CDK5RAP2 CTSB DSCAML1 DUSP22 FOS GRIN3A HMGA2 MET MICALCL MLLT3 MSH3 PARK2 PAX8 PDCD4 PPAP2B TGFBR3 ASNS B4GALT1 Clorf68 CSDE1 CTBP2 DAD1 EDN3 EMP1 EPOR FAT3 FKBP1A FOXE1 HLA-DOA HOXC10 HOXC11 HOXC12 HOXC13 HOXC4 HOXC5 HOXC6 HOXC8 HOXC9 KRT71 LCE1C LCE1E LCE3B LCE3C LCE3E LCE6A LRRK2 MYT1 NOBOX NOTCH2 NOTCH2NL NPNT NRAS SCIN SLC5A3 ZFAND5

List 13: tissue development, n=19: TGFBR3 B4GALT1 Clorf68 EMP1 EPOR FKBP1A FOXE1 HOXC11 HOXC13 HOXC4 KRT71 LCE1C LCE1E LCE3B LCE3C LCE3E LCE6ANRAS ZFAND5

List 14: regulation of cellular process, n=100: AFAP1L2 AFF1 AGTR1 ARHGEF5 CDK5RAP2 CTSB DEK DMAP1 DUSP22 EBAG9 EPS8 FOS GNA14 GNG10 GPR39 HMGA2 IL18R1 KRT8 LMO3 MCTP2 MET MLLT3 MSH3 NEDD4L OPRM1 OR2T3 PAX8 PDCD4 PDE8B PPAP2B PPP2R5E PSD4 RAF1 RAP 1A RCBTB1 SPATA13 TGFBR3 ZNF253 ASNS B4GALT1 CNTNAP4 CSDE1 CTBP2 DAD1 DTX4 EDN3 EIF4E3 ENY2 EPOR FKBP1A FOXE1 FOXO6 GOLT1B GPR45 HLA-DOA HNF4A HOXC10 HOXC11 HOXC12 HOXC13 HOXC4 HOXC5 HOXC6 HOXC8 HOXC9 IL1RL1 INSIG1 IQSEC1 IQSEC3 LOC400713 LRRK2 MYT1 NFIB NOBOX NOTCH2 NOTCH2NL NRAS OR10J5 OR2A42 OR2A5 OR2T34 OR51A2 OR51A4 OR5A2 OR5B21 OR6J1 PTPRD RSPO4 SCIN SESN1 SIRPB1 SMARCB1 ST18 TTF2 VGLL4 ZFAND5 ZNF486 ZNF610 ZNF626 ZNF826

List 15: system development, n=41: ABLIM1 AGTR1 CD9 CDK5RAP2 DSCAML1 FOS GRIN3A MET MSH3 PARK2 PPAP2B TGFBR3 B4GALT1 Clorf68 CSDE1 EMP1 EPOR FKBP1A FOXE1 HLA-DOA HOXC10 HOXC11 HOXC13 HOXC4 HOXC5 HOXC6 HOXC8 HOXC9 KRT71 LCE1C LCE1E LCE3B LCE3C LCE3E LCE6A MYT1 NOTCH2 NRAS SCIN SLC5A3 ZFAND5

List 16: regulation of biological process, n=102: AFAP1L2 AFF1 AGTR1 ARHGEF5 CDK5RAP2 CTSB DEK DMAP1 DUSP22 EBAG9 EPS8 FOS GNA14 GNG10 GPR39 GRIN3A HMGA2 IL18R1 KRT8 LMO3 MCTP2 MET MLLT3 MSH3 NEDD4L OPRM10R2T3 PAX8 PDCD4 PDE8B PPAP2B PPP2R5E PSD4 RAF1 RAP1A RCBTB1 SPATA13 TGFBR3 ZNF253 ASNS B4GALT1 CNTNAP4 CSDE1 CTBP2 DAD1 DTX4 EDN3 EIF4E3 ENY2 EPOR FKBP1A FOXE1 FOXO6 GOLT1B GPR45 HLA-DOA HNF4A HOXC10 HOXC11 HOXC12 HOXC13 HOXC4 HOXC5 HOXC6 HOXC8 HOXC9 IL1RL1 INSIG1 IQSEC1 IQSEC3 LOC400713 LRRK2 MYT1 NFIB NOBOX NOTCH2 NOTCH2NL NRAS OR10J5 OR2A42 OR2A5 OR2T34 OR51A2 OR51A4 OR5A2 OR5B21 OR6J1 PTPRD RSPO4 SCIN SESN1 SIRPB1 SLC5A3 SMARCB1 ST18 TTF2 VGLL4 ZFAND5 ZNF486 ZNF610 ZNF626 ZNF826

List 17: anatomical structure development, n=43: ABLIM1 AGTR1 CD9 CDK5RAP2 DSCAML1 FOS GRIN3A MET MSH3 PARK2 PAX8 PPAP2B TGFBR3 B4GALT1 Clorf68 CSDE1 DAD1 EMP1 EPOR FKBP1A FOXE1 HLA-DOA HOXC10 HOXC11 HOXC13 HOXC4 HOXC5 HOXC6 HOXC8 HOXC9 KRT71 LCE1C LCE1E LCE3B LCE3C LCE3E LCE6A MYT1 NOTCH2 NRAS SCIN SLC5A3 ZFAND5

List 18: biological regulation, n=105: AFAP1L2 AFF1 AGTR1 ARHGEF5 CD9 CDK5RAP2 CTSB DEK DMAP1 DUSP22 EBAG9 EPS8 FOS GNA14 GNG10 GPR39 GRIN3A HMGA2 IL18R1 KRT8 LMO3 MCTP2 MET MLLT3 MSH3 NEDD4L OPRM1 OR2T3 PAX8 PDCD4 PDE8B PPAP2B PPP2R5E PSD4 RAF1 RAP1A RCBTB1 SPATA13 TGFBR3 ZNF253 ASNS B4GALT1 CNTNAP4 CSDE1 CTBP2 DAD1 DTX4 EDN3 EIF4E3 EMP1 ENY2 EPOR FKBP1A FOXE1 FOXO6 GOLT1B GPR45 HLA-DOA HNF4A HOXC10 HOXC11 HOXC12 HOXC13 HOXC4 HOXC5 HOXC6 HOXC8 HOXC9 IL1RL1 INSIG1 IQSEC1 IQSEC3 LOC400713 LRRK2 MYT1 NFIB NOBOX NOTCH2 NOTCH2NL NRAS OR10J5 OR2A42 OR2A5 OR2T34 OR51A2 OR51A4 OR5A2 OR5B21 OR6J1 PLSCR1 PTPRD RSPO4 SCIN SESN1 SIRPB1 SLC5A3 SMARCB1 ST18 TTF2 VGLL4 ZFAND5 ZNF486 ZNF610 ZNF626 ZNF826

List 19: pattern specification process, n=10: DSCAML1 MLLT3 HOXC10 HOXC11 HOXC13 HOXC4 HOXC5 HOXC6 HOXC8 HOXC9

List 20: keratinocyte differentiation, n=6: LCE1C LCE1E LCE3B LCE3C LCE3E LCE6A

List 21: epidermal cell differentiation, n=6: LCE1C LCE1E LCE3B LCE3C LCE3E LCE6A

List 22: organ development, n=31: ABLIM1 AGTR1 CDK5RAP2 DSCAML1 MET PPAP2B TGFBR3 B4GALT1 Clorf68 CSDE1 EMP1 EPOR FKBP1A FOXE1 HLA-DOA HOXC11 HOXC13 HOXC4 HOXC8 HOXC9 KRT71 LCE1C LCE1E LCE3B LCE3C LCE3E LCE6A NOTCH2 NRAS SCIN ZFAND5

List 23: sequence-specific DNA binding, n=16: FOS HMGA2 MSH3 FOXE1 FOXO6 HNF4A HOXC10 HOXC11 HOXC12 HOXC13 HOXC4 HOXC5 HOXC6 HOXC8 HOXC9 NOBOX

List 24: regulation of transcription, DNA-dependent, n=33: AFAP1L2 AFF1 DEK FOS HMGA2 MLLT3 PAX8 PDE8B ZNF253 CSDE1 ENY2 FOXE1 FOXO6 HNF4A HOXC10 HOXC11 HOXC12 HOXC13 HOXC4 HOXC5 HOXC6 HOXC8 HOXC9 LOC400713 MYT1 NFIB NOBOX NOTCH2 SMARCB1 ST18 ZNF486 ZNF610 ZNF626

List 25: embryonic development, n=13: DSCAML1 MLLT3 PPAP2B DAD1 EPOR FOXE1 HOXC10 HOXC11 HOXC4 HOXC5 HOXC6 HOXC9 ZFAND5

List 26: regulation of RNA metabolic process, n=33: AFAP1L2 AFF1 DEK FOS HMGA2 MLLT3 PAX8 PDE8B ZNF253 CSDE1 ENY2 FOXE1 FOXO6 HNF4A HOXC10 HOXC11 HOXC12 HOXC13 HOXC4 HOXC5 HOXC6 HOXC8 HOXC9 LOC400713 MYT1 NFIB NOBOX NOTCH2 SMARCB1 ST18 ZNF486 ZNF610 ZNF626

List 27: ARF guanyl-nucleotide exchange factor activity, n=3: PSD4 IQSEC1 IQSEC3

List 28: transcription, DNA-dependent, n=34: AFAP1L2 AFF1 DEK FOS HMGA2 MLLT3 PAX8 PDE8B ZNF253 CSDE1 ENY2 FOXE1 FOXO6 HNF4A HOXC10 HOXC11 HOXC12 HOXC13 HOXC4 HOXC5 HOXC6 HOXC8 HOXC9 LOC400713 MYT1 NFIB NOBOX NOTCH2 SMARCB1 ST18 TTF2 ZNF486 ZNF610 ZNF626

List 29: RNA biosynthetic process, n=34: AFAP1L2, AFF1, DEK, FOS, HMGA2, MLLT3, PAX8, PDE8B, ZNF253, CSDE1, ENY2, FOXE1, FOXO6, HNF4A, HOXC10, HOXC11, HOXC12, HOXC13, HOXC4, HOXC5, HOXC6, HOXC8, HOXC9, LOC400713, MYT1, NFIB, NOBOX, NOTCH2, SMARCB1, ST18, TTF2, ZNF486, ZNF610, ZNF626

List 30: cell surface receptor linked signal transduction, n-34: AFAP1L2, AGTR1, DUSP22, EPS8, FOS, GNA14, GNG10, GPR39, KRT8, MET, OPRM1, OR2T3, PPAP2B, RAF1, TGFBR3, DTX4, EDN3, FKBP1A, GPR45, NOTCH2, NOTCH2NL, OR10J5, OR2A42, OR2A5, OR2T34, OR51A2, OR51A4, OR5A2, OR5B21, OR6J1, PTPRD, RSPO4, SIRPB1, ZFAND5

List 31: signal transducer activity, n=37: AGTR1, EPS8, GNA14, GNG10, GPR39, GRIN3A, IL18R1, MET, OPRM1, OR2T3, PAX8, PDE8B, PKHD1L1, RAF1, SNX25, TGFBR3, TRA@, ZP4, ANTXRL, EPOR, FKBP1A, GOLT1B, GPR45, HLA-DOA, HNF4A, IL1RL1, NOTCH2, OR10J5, OR2A42, OR2A5, OR2T34, OR51A2, OR51A4, OR5A2, OR5B21, OR6J1, PTPRD

List 32: N-methyl-D-aspartate selective glutamate receptor complex, n=2: EPS8, GRIN3A

List 33: molecular transducer activity: AGTR1, EPS8, GNA14, GNG10, GPR39, GRIN3A, IL18R1, MET, OPRM1, OR2T3, PAX8, PDE8B, PKHD1L1, RAF1, SNX25, TGFBR3, TRA@, ZP4, ANTXRL, EPOR, FKBP1A, GOLT1B, GPR45, HLA-DOA, HNF4A, IL1RL1, NOTCH2, OR10J5, OR2A42, OR2A5, OR2T34, OR51A2, OR51A4, OR5A2, OR5B21, OR6J1, PTPRD

List 34: heart trabecula formation, n=2: TGFBR3, FKBP1A

In some embodiments, genes or markers that are localized to a particular chromosome can be useful for the present invention. The present invention can be practiced by determining the presence or absence of a combination of polymorphism and creating a panel of biomarkers. The biomarkers in each panel may be combined for increased accuracy, sensitivity, and/or specificity. In some embodiments, the biomarkers within each panel are interchangeable (modular). The plurality of biomarkers in all panels can be substituted, increased, reduced, or improved to accommodate the definition of new pathologic subtypes (e.g. new case reports of metastasis to the thyroid from other organs). Chromosomal localization of theyroid markers are shown below:

List 35: ADAMTS18, LOC129656, OR2A9P, OR2T34, ENST00000361820, LMO3, OR7E35P, ZFAND5, ACSM2, B4GALT1, ENST00000237937, ENST00000345125, NPNT, PAX8, PHF11, XM 001132965, ACSM2A, ENST00000307431, MYT1, C8orf79, ENST00000385399, GPR39, ENST00000358816, FDFT1, GSTCD

List 36: AK129935, C20orf69, CTBP2, ENST00000334383, KPRP, MGC16169, AK091978, ENST00000219054, LCE3B, LCE3C, RCBTB1, IYD, DUSP22, LCE6A, OR2A5, LCE3E, EPOR, OPRM1, OR2A20P, C9orf156, ENY2, PCMTD2, ARHGEFS, TMC1, CTSB

List 37: ENST00000384854, ENST00000337573, BC043197, EDN3, FOXO6, LOC728377, PIP3-E, PKHD1L1, PPP3R2, SNX25, ENST00000398221, Clorf68, EBAG9, IQSEC3, LRRK2, ZNF826, RMST, SCIN, GRIN3A, CR619628, FOXE1, C20orf174, ENST00000353047, LCE1E, MRPS11

List 38: MRPL46, OR2T3, AFAP1L2, LOC392352, ZNF486, MGST1, GCNT1, SIRPB1, ZNF626, CNTNAP4, ENST00000329697, ACSM2B, LOC645433, MON1B, OR2A42, GPR103, TGFBR3, PDE8B, PSD4, LMO3, LOC129656, GSTCD, CTBP2, CTSB, ENST00000337573

List 39: OR2A5, OR7E35P, C8orf79, EDN3, IQSEC3, ENST00000345125, GPR103, LOC129656, ENST00000385399, XM 001132965, LCE1E, DUSP22, ADAMTS18, OR2T34, PAX8, ENY2, OR2T3, PCMTD2, LCE3C, OPRM1, ENST00000358816, ZNF826, GCNT1, LCE6A, EBAG9

List 40: PDE8B, LOC728377, ACSM2, ENST00000334383, ENST00000219054, AK129935, AFAP1L2, CTSB, KPRP, PSD4, SIRPB1, LCE3B, AK091978, RCBTB1, ACSM2A, CNTNAP4, OR2A42, FOXO6, ZNF486, ZNF626, BC043197, FOXE1, GPR39, CR619628, LOC392352

List 41: Clorf68, ENST00000237937, ARHGEFS, NPNT, PHF11, C20orf174, EPOR, MYT1, GSTCD, LRRK2, PKHD1L1, ENST00000353047, MGST1, LOC645433, TMC1, C20orf69, MRPL46, SNX25, RMST, MON1B, ZFANDS, ACSM2B, SCIN, OR2A20P, LMO3

List 42: C9orf156, ENST00000384854, ENST00000361820, B4GALT1, IYD, FDFT1, CTBP2, PIP3-E, MRPS11, ENST00000337573, PPP3R2, ENST00000329697, MGC16169, TGFBR3, LCE3E, GRIN3A, ENST00000398221, OR2A9P, ENST00000307431, ADAMTS18, SIRPB1, CNTNAP4, ARHGEFS, AFAP1L2, ACSM2B

List 43: ACSM2A, RMST, ZNF486, SNX25, TGFBR3, OR2T3, PCMTD2, ENST00000398221, AK129935, ENST00000329697, OR2A9P, MRPL46, MGC16169, LCE3E, ENST00000219054, NPNT, PHF11, GPR103, C20orf69, PAX8, RCBTB1, C20orf174, EDN3, OR2T34, PSD4

List 44: TMC1, ENY2, FOXE1, EPOR, C9orf156, PPP3R2, PDE8B, ENST00000345125, ZNF826, ACSM2, ZNF626, ENST00000361820, C8orf79, LCE6A, LOC645433, LCE1E, OPRM1, LCE3B, OR2A42, BC043197, CR619628, MGST1, IQSEC3, ENST00000334383, GPR39

List 45: ABLIM1, ADAMTS18, AFAP1L2, AFF1, AGTR1, ARHGEFS, C8orf79, C9orf156, CD9, CDK5RAP2, CTSB, DEK, DMAP1, DSCAML1, DUSP22, DYSF, EBAG9, EPS8, FDFT1, FOS, FRMD3, GLCCI1, GNA14, GNG10, GPR39, GRIN3A, GSTCD, HMGA2, IL18R1, IYD, KCNIP4, KRT8, LINGO2, LMO3, L0051233, MCTP2, MET, MGC16169, MGST1, MICAL2, MICALCL, MLLT3, MSH3, NEDD4L, NSUN2, OLFML1, OPRM1, OR2T3, OR7E37P, PARK2, PAX8, PDCD4, PDE8B, PDLIM4, PHF11, PIP3-E, PKHD1L1, PPAP2B, PPFIBP2, PPP2R5E, PSD4, RAF1, RAP1A, RBMS3, RCBTB1, SLC2A13, SNX25, SPATA13, ST3GAL5, TGFBR3, TRA@, ZNF253, ZP4.

Business Methods

As described herein, the term customer or potential customer refers to individuals or entities that may utilize methods or services of the molecular profiling business. Potential customers for the molecular profiling methods and services described herein include for example, patients, subjects, physicians, cytological labs, health care providers, researchers, insurance companies, government entities such as Medicaid, employers, or any other entity interested in achieving more economical or effective system for diagnosing, monitoring and treating cancer.

Such parties can utilize the molecular profiling results, for example, to selectively indicate expensive drugs or therapeutic interventions to patients likely to benefit the most from said drugs or interventions, or to identify individuals who would not benefit or may be harmed by the unnecessary use of drugs or other therapeutic interventions.

I. Methods of Marketing

The services of the molecular profiling business of the present invention may be marketed to individuals concerned about their health, physicians or other medical professionals, for example as a method of enhancing diagnosis and care; cytological labs, for example as a service for providing enhanced diagnosis to a client; health care providers, insurance companies, and government entities, for example as a method for reducing costs by eliminating unwarranted therapeutic interventions. Methods of marketing to potential clients, further includes marketing of database access for researchers and physicians seeking to find new correlations between DNA polymorphisms and diseases or conditions.

The methods of marketing may include the use of print, radio, television, or internet based advertisement to potential customers. Potential customers may be marketed to through specific media, for example, endocrinologists may be marketed to by placing advertisements in trade magazines and medical journals including but not limited to The Journal of the American Medical Association, Physicians Practice, American Medical News, Consultant, Medical Economics, Physician's Money Digest, American Family Physician, Monthly Prescribing Reference, Physicians' Travel and Meeting Guide, Patient Care, Cortlandt Forum, Internal Medicine News, Hospital Physician, Family Practice Management, Internal Medicine World Report, Women's Health in Primary Care, Family Practice News, Physician's Weekly, Health Monitor, The Endocrinologist, Journal of Endocrinology, The Open Endocrinology Journal, and The Journal of Molecular Endocrinology. Marketing may also take the form of collaborating with a medical professional to perform experiments using the methods and services of the present invention and in some cases publish the results or seek funding for further research. In some cases, methods of marketing may include the use of physician or medical professional databases such as, for example, the American Medical Association (AMA) database, to determine contact information.

In one embodiment methods of marketing comprises collaborating with cytological testing laboratories to offer a molecular profiling service to customers whose samples cannot be unambiguously diagnosed using routine methods.

II. Business Methods Utilizing a Computer

The molecular profiling business may utilize one or more computers in the methods of the present invention such as a computer 800 as illustrated in FIG. 5. The computer 800 may be used for managing customer and sample information such as sample or customer tracking, database management, analyzing molecular profiling data, analyzing cytological data, storing data, billing, marketing, reporting results, or storing results. The computer may include a monitor 807 or other graphical interface for displaying data, results, billing information, marketing information (e.g. demographics), customer information, or sample information. The computer may also include means for data or information input 816, 815. The computer may include a processing unit 801 and fixed 803 or removable 811 media or a combination thereof. The computer may be accessed by a user in physical proximity to the computer, for example via a keyboard and/or mouse, or by a user 822 that does not necessarily have access to the physical computer through a communication medium 805 such as a modem, an internet connection, a telephone connection, or a wired or wireless communication signal carrier wave. In some cases, the computer may be connected to a server 809 or other communication device for relaying information from a user to the computer or from the computer to a user. In some cases, the user may store data or information obtained from the computer through a communication medium 805 on media, such as removable media 812.

The molecular profiling business may enter sample information into a database for the purpose of one or more of the following: inventory tracking, assay result tracking, order tracking, customer management, customer service, billing, and sales. Sample information may include, but is not limited to: customer name, unique customer identification, customer associated medical professional, indicated assay or assays, assay results, adequacy status, indicated adequacy tests, medical history of the individual, preliminary diagnosis, suspected diagnosis, sample history, insurance provider, medical provider, third party testing center or any information suitable for storage in a database. Sample history may include but is not limited to: age of the sample, type of sample, method of acquisition, method of storage, or method of transport.

The database may be accessible by a customer, medical professional, insurance provider, third party, or any individual or entity which the molecular profiling business grants access. Database access may take the form of electronic communication such as a computer or telephone. The database may be accessed through an intermediary such as a customer service representative, business representative, consultant, independent testing center, or medical professional. The availability or degree of database access or sample information, such as assay results, may change upon payment of a fee for products and services rendered or to be rendered. The degree of database access or sample information may be restricted to comply with generally accepted or legal requirements for patient or customer confidentiality. The molecular profiling company may bill the individual, insurance provider, medical provider, or government entity for one or more of the following: sample receipt, sample storage, sample preparation, cytological testing, molecular profiling, input and update of sample information into the database, or database access.

III. Business Flow

FIG. 2 is a flow chart illustrating one way in which samples might be processed by the molecular profiling business. FIG. 2A depicts one embodiment of a way in which samples might be processed by a molecular profiling business of the present invention. Samples of thyroid cells, for example, may be obtained by an endocrinologist perhaps via fine needle aspiration 100. Samples are subjected to routine cytological staining procedures 125. Said routine cytological staining provides four different possible preliminary diagnoses non-diagnostic 105, benign 110, ambiguous or suspicious 115, or malignant 120. The molecular profiling business may then analyze genomic DNA, gene product expression levels, alternative gene product exon usage or a combination thereof as described herein 130. Said analysis of genomic DNA, gene product expression levels, alternative gene product exon usage or a combination thereof (molecular profiling) may lead to a definitive diagnosis of malignant 140 or benign 135. In some cases only a subset of samples are analyzed by molecular profiling such as those that provide ambiguous and non-diagnostic results during routine cytological examination.

In some cases the molecular profiling results confirms the routine cytological test results. In other cases, the molecular profiling results differ. In such cases, samples may be further tested, data may be reexamined, or the molecular profiling results or cytological assay results may be taken as the correct diagnosis. Benign diagnoses may also include diseases or conditions that, while not malignant cancer, may indicate further monitoring or treatment. Similarly, malignant diagnoses may further include diagnosis of the specific type of cancer or a specific metabolic or signaling pathway involved in the disease or condition. Said diagnoses, may indicate a treatment or therapeutic intervention such as radioactive iodine ablation, surgery, thyroidectomy; or further monitoring.

Kits

The molecular profiling business may provide a kit for obtaining a suitable sample. Said kit 203 as depicted in FIG. 3 may comprise a container 202, a means for obtaining a sample 200, reagents for storing the sample 205, and instructions for use of said kit. In another embodiment, the kit further comprises reagents and materials for performing the molecular profiling analysis. In some cases, the reagents and materials include a computer program for analyzing the data generated by the molecular profiling methods. In still other cases, the kit contains a means by which the biological sample is stored and transported to a testing facility such as the molecular profiling business or a third party testing center.

The molecular profiling business may also provide a kit for performing molecular profiling. Said kit may comprise a means for extracting protein or nucleic acids including all necessary buffers and reagents; and, a means for analyzing levels of protein or nucleic acids including controls, and reagents. The kit may further comprise software or a license to obtain and use software for analysis of the data provided using the methods and compositions of the present invention.

EXAMPLES Example 1 Hybridization Analysis on Thyroid Samples

In order to identify genomic regions that distinguish malignant thyroid nodules from benign, we examined 86 hybridizations to the HumanSNP 6.0 (SNP6.0) array and followed the traditional single nucleotide polymorphism (SNP) and copy number analysis with a novel downstream analysis method. The preliminary results of the SNP6.0 array were used as input into an algorithm that allowed the full characterization of DNA copy number aberrations in our cohort. Here we describe i) the exact chromosomal location, ii) genomic region size, and iii) nature of aberration (copy number gain and/or copy number loss) for each of the genomic regions that we have discovered to distinguish malignant thyroid nodules from benign.

Several comparisons were performed between groups of samples based on subtype thyroid nodule pathology. The first of four comparisons focused on follicular carcinoma versus follicular adenoma (FC vs. FA) and resulted in 47 significant genomic regions. A similar comparison between follicular variant of papillary thyroid carcinoma and nodular hyperplasia (FVPTC vs. NHP) resulted in 30 significant genomic regions, while papillary thyroid carcinoma versus nodular hyperplasia (PTC vs. NHP) resulted in 12 significant genomic regions. The fourth comparison grouped all the malignant and compared to a group of all the benign samples. This analysis resulted in 250 significant genomic regions. In sum, we identify a total of 339 genomic regions that show distinct genomic copy number differences between benign and malignant thyroid samples. These genomic regions map to at least 740 unique genes and/or proteins.

Methods

Microarray and Dataset

A total of 86 thyroid samples were examined with the Affymetrix HumanSNP 6.0 DNA array to identify genes that differed significantly in copy number between benign and malignant samples. Samples were classified according to thyroid pathology: follicular adenomas (FA, n=20), Hurthle cell adenomas (HA, n=10), lymphocytic thyroiditis (LCT, n=5), and nodular hyperplasia (NHP, n=20) were studied. When grouped, these samples were classified as benign (n=55). Similarly, follicular carcinoma (FC, n=4), follicular variant of papillary thyroid carcinoma (FVPTC, n=12), and papillary thyroid carcinoma (PTC, n=15) were also examined and classified as malignant (n=31) when grouped.

Affymetrix software was used to extract, normalize, and summarize SNP6.0 array intensity data from approximately 1.8 million markers encompassing 744,000 probes evenly spaced along the human genome. The median intensity of each probe across all 86 arrays and a dataset of publicly available reference HAPMAP3 CEL files (www.HAPMAP.org) were used together as a reference set, where this reference set represented the normal copy number of a given probe. The relative intensity of a given probe in a sample against the reference set was then used to determine whether a copy number aberration was present. An increase in relative intensity was indicative of copy number gain (amplification), and a decrease in relative intensity was indicative of copy number loss (deletion).

The resulting SNP6.0 relative intensities for each sample were then translated into segments (regions of equal copy number data) using circular binary segmentation (CBS) (Olshen, Venkatraman et al. 2004). These segments were then used to create non-overlapping features among samples using PLINK-a free whole genome association analysis toolset (Purcell, Neale et al. 2007). Top features associated with disease label were identified using chi-square tests and PLINK. Classification was performed using top PLINK features and support vector machine (SVM) analysis. The CEL input file name for each sample is listed in Table 2.

Table 2. Samples analyzed and pathologic classification. Input CEL files are listed along with specific thyroid subtype and simplified pathology classification of each sample. Thyroid subtypes are follicular adenoma (FA), Hurthle cell adenoma (HA), lymphocytic thyroiditis (LCT), nodular hyperplasia (NHP), follicular carcinoma (FC), follicular variant of papillary thyroid carcinoma (FVPTC), and papillary carcinoma (PTC).

TABLE 2 Simplified CEL File Name Pathology Pathology VCP00101_FA.CEL FA Benign VCP00102_FA.CEL FA Benign VCP00103_FA.CEL FA Benign VCP00104_FA.CEL FA Benign VCP00105_FA.CEL FA Benign VCP00106_FA.CEL FA Benign VCP00107_FA.CEL FA Benign VCP00108_FA.CEL FA Benign VCP00109_FA.CEL FA Benign VCP00110_FA.CEL FA Benign VCP00111_FA.CEL FA Benign VCP00112_FA.CEL FA Benign VCP00113_FA.CEL FA Benign VCP00114_FA.CEL FA Benign VCP00115_FA.CEL FA Benign VCP00116_FA.CEL FA Benign VCP00117_FA.CEL FA Benign VCP00118_FA.CEL FA Benign VCP00119_FA.CEL FA Benign VCP00120_FA.CEL FA Benign VCP00121_HA.CEL HA Benign VCP00122_HA.CEL HA Benign VCP00123_HA.CEL HA Benign VCP00124_HA.CEL HA Benign VCP00125_HA.CEL HA Benign VCP00126_HA.CEL HA Benign VCP00127_HA.CEL HA Benign VCP00128_HA.CEL HA Benign VCP00129_HA.CEL HA Benign VCP00130_HA.CEL HA Benign VCP00131_LCT.CEL LCT Benign VCP00132_LCT.CEL LCT Benign VCP00133_LCT.CEL LCT Benign VCP00134_LCT.CEL LCT Benign VCP00135_LCT.CEL LCT Benign VCP00136_NHP.CEL NHP Benign VCP00137_NHP.CEL NHP Benign VCP00138_NHP.CEL NHP Benign VCP00139_NHP.CEL NHP Benign VCP00140_NHP.CEL NHP Benign VCP00141_NHP.CEL NHP Benign VCP00142_NHP.CEL NHP Benign VCP00143_NHP.CEL NHP Benign VCP00144_NHP.CEL NHP Benign VCP00145_NHP.CEL NHP Benign VCP00146_NHP.CEL NHP Benign VCP00147_NHP.CEL NHP Benign VCP00148_NHP.CEL NHP Benign VCP00149_NHP.CEL NHP Benign VCP00150_NHP.CEL NHP Benign VCP00151_NHP.CEL NHP Benign VCP00152_NHP.CEL NHP Benign VCP00153_NHP.CEL NHP Benign VCP00154_NHP.CEL NHP Benign VCP00155_NHP.CEL NHP Benign VCP00156_FC.CEL FC Malignant VCP00157_FC.CEL FC Malignant VCP00158_FC.CEL FC Malignant VCP00159_FC.CEL FC Malignant VCP00160_FVPTC.CEL FVPTC Malignant VCP00161_FVPTC.CEL FVPTC Malignant VCP00162_FVPTC.CEL FVPTC Malignant VCP00163_FVPTC.CEL FVPTC Malignant VCP00164_FVPTC.CEL FVPTC Malignant VCP00165_FVPTC.CEL FVPTC Malignant VCP00166_FVPTC.CEL FVPTC Malignant VCP00167_FVPTC.CEL FVPTC Malignant VCP00168_FVPTC.CEL FVPTC Malignant VCP00169_FVPTC.CEL FVPTC Malignant VCP00170_FVPTC.CEL FVPTC Malignant VCP00171_FVPTC.CEL FVPTC Malignant VCP00172_PTC.CEL PTC Malignant VCP00173_PTC.CEL PTC Malignant VCP00174_PTC.CEL PTC Malignant VCP00175_PTC.CEL PTC Malignant VCP00176_PTC.CEL PTC Malignant VCP00177_PTC.CEL PTC Malignant VCP00178_PTC.CEL PTC Malignant VCP00179_PTC.CEL PTC Malignant VCP00180_PTC.CEL PTC Malignant VCP00181_PTC.CEL PTC Malignant VCP00182_PTC.CEL PTC Malignant VCP00183_PTC.CEL PTC Malignant VCP00184_PTC.CEL PTC Malignant VCP00185_PTC.CEL PTC Malignant VCP00186_PTC.CEL PTC Malignant

Results

Novel DNA Copy Number Analysis in Thyroid

In order to identify genomic regions that distinguish malignant thyroid nodules from benign, we examined 86 hybridizations to the SNP 6.0 array and used those preliminary results as an input dataset into a novel analysis algorithm. Genes were mapped to microarray sequences using Affymetrix annotation file GenomeWideSNP_(—)6.na26, based on Human Genome Build 18. The first of four comparisons focused on follicular carcinoma (FC, n=4) versus follicular adenoma (FA, n=20). These analyses resulted in the identification of 47 statistically significant genomic regions (p<0.05), which mapped to 186 known genes and/or proteins (Table 3).

A similar comparison was performed between follicular variants of papillary carcinoma (FVPTC, n=12) and samples categorized as nodular hyperplasia (NHP, n=20). This analysis resulted in the identification of 30 significant genomic regions, which mapped to 49 known genes and/or proteins (Table 4). A third analysis aimed at papillary thyroid carcinoma (PTC, n=15) compared to NHP (n=20) resulted in the identification of 12 significant genomic regions, which mapped to 15 known genes and/or proteins (Table 5).

The fourth analysis grouped data from all available malignant samples in this cohort (n=31) and compared them to a group of all available benign samples (n=55). This analysis resulted in the identification of 250 significant genomic regions, which mapped to 561 known genes and/or proteins (Table 6).

In sum, we identify a total of 339 genomic regions that show distinct genomic copy number differences between different subgroups of benign and malignant samples. These genomic regions map to 740 known human genes and/or proteins.

Table 3. Top 47 genomic regions that differentiate Follicular Carcinoma (Fc) from Follicular Adenoma (FA). A significance filter of p<0.05 was used, followed by ranking in descending order. PLINK features in FC (n=4) were compared to FA (n=20). The 47 genomic regions that differentiate FC from FA are mapped to 56 known genes and/or proteins.

Genomic Region, chromosome number and start of genomic position of a given PLINK feature; P, p-value of group comparison for a given PLINK feature, Total FC with Feature, number of FC samples harboring a given PLINK feature; Total FA with Feature, number of FA samples harboring a given PLINK feature; Genomic Size, size of a given PLINK feature; Gene, name of known genes mapped to a given genomic region. Single genomic regions often coded for multiple genes and/or proteins. Similarly, several genomic regions (or significant PLINK features) often mapped next to one another and consequently mapped to the same gene or genes.

TABLE 3 Total Total FC with FA with Nature of Genomic Feature Feature Genomic Region P n = 4 n = 20 Genomic Size Feature Gene Description p2-113677467 0.0020 3 0 77353 loss PAX8 Homo sapiens paired box 8 (PAX8), transcript variant PAX8A, mRNA. p2-113677467 0.0020 3 0 77353 loss PSD4 Pleckstrin and Sec7 domain containing 4 p6-150752141 0.0020 3 0 25281 loss IYD Homo sapiens iodotyrosine deiodinase (IYD), mRNA. p6-150731443 0.0075 3 1 20698 loss IYD Homo sapiens iodotyrosine deiodinase (IYD), mRNA. p1-150852730 0.0125 4 5 121 gain p1-151026302 0.0189 3 2 2234 gain or loss C1orf68 chromosome 1 open reading frame 68 p1-151026302 0.0189 3 2 2234 gain or loss KPRP keratinocyte proline-rich protein p1-151026302 0.0189 3 2 2234 gain or loss LCE1E Homo sapiens late cornified envelope 1E (LCE1E), mRNA. p1-151026302 0.0189 3 2 2234 gain or loss LCE6A late cornified envelope 6A p1-150822318 0.0195 4 6 30412 gain LCE3B Late cornified envelope 3B p1-150822318 0.0195 4 6 30412 gain LCE3C Late cornified envelope 3C p1-150822318 0.0195 4 6 30412 gain LCE3E Late cornified envelope 3E p10-116098703 0.0211 2 0 23751 loss AFAP1L2 Homo sapiens actin filament associated protein 1-like 2 (AFAP1L2), transcript variant 1, mRNA. p10-116098703 0.0211 2 0 23751 loss ENST00000304129 KIAA1914 (KIAA1914), transcript variant 1, mRNA [Source: RefSeq_dna; Acc: NM_001001936] p12-16629888 0.0211 2 0 38237 loss LMO3 Homo sapiens LIM domain only 3 (rhombotin- like 2) (LMO3), transcript variant 2, mRNA. p12-16629888 0.0211 2 0 38237 loss MGST1 microsomal glutathione S-transferase 1 p13-48992710 0.0211 2 0 28879 loss PHF11 Homo sapiens PHD finger protein 11 (PHF11), transcript variant 2, mRNA. p13-48992710 0.0211 2 0 28879 loss RCBTB1 Homo sapiens regulator of chromosome condensation (RCC1) and BTB (POZ) domain containing protein 1 (RCBTB1), mRNA. p1-41607648 0.0211 2 0 4282 loss FOXO6 forkhead box protein O6 p15-86805992 0.0211 2 0 42408 loss MRPL46 Homo sapiens mitochondrial ribosomal protein L46 (MRPL46), nuclear gene encoding mitochondrial protein, mRNA. p15-86805992 0.0211 2 0 42408 loss MRPS11 Homo sapiens mitochondrial ribosomal protein S11 (MRPS11), nuclear gene encoding mitochondrial protein, transcript variant 1, mR p1-91961359 0.0211 2 0 29475 loss TGFBR3 Homo sapiens transforming growth factor, beta receptor III (TGFBR3), mRNA. p20-1500162 0.0211 2 0 5017 gain SIRPB1 Homo sapiens signal-regulatory protein beta 1 (SIRPB1), transcript variant 1, mRNA. p20-57293085 0.0211 2 0 40059 loss C20orf174 Chromosome 20 open reading frame 174 p20-57293085 0.0211 2 0 40059 loss EDN3 Homo sapiens endothelin 3 (EDN3), transcript variant 2, mRNA. p2-113671350 0.0211 2 0 6118 loss PSD4 Homo sapiens pleckstrin and Sec7 domain containing 4 (PSD4), mRNA. p4-107043531 0.0211 2 0 77398 loss GSTCD Glutathione S-transferase, C-terminal domain containing p4-107043531 0.0211 2 0 77398 loss MGC16169 Homo sapiens hypothetical protein MGC16169 (MGC16169), mRNA. p4-107043531 0.0211 2 0 77398 loss NPNT Homo sapiens nephronectin (NPNT), mRNA. p4-186393053 0.0211 2 0 63977 loss SNX25 Homo sapiens sorting nexin 25 (SNX25), mRNA. p5-164373590 0.0211 2 0 38230 loss p8-110432695 0.0211 2 0 95372 loss ENY2 Enhancer of yellow 2 homolog (Drosophila) p8-110432695 0.0211 2 0 95372 loss EPOR erythropoietin receptor p8-110432695 0.0211 2 0 95372 loss PKHD1L1 Homo sapiens polycystic kidney and hepatic disease 1 (autosomal recessive)-like 1 (PKHD1L1), mRNA. p8-110560174 0.0211 2 0 65772 loss EBAG9 Homo sapiens estrogen receptor binding site associated, antigen, 9 (EBAG9), transcript variant 1, mRNA. p8-110560174 0.0211 2 0 65772 loss ENST00000337573 Receptor-binding cancer antigen expressed on SiSo cells (Cancer-associated surface antigen RCAS1) (Estrogen receptor-binding fr p8-110560174 0.0211 2 0 65772 loss FDFT1 farnesyl-diphosphate farnesyltransferase 1 p8-110560174 0.0211 2 0 65772 loss PKHD1L1 Homo sapiens polycystic kidney and hepatic disease 1 (autosomal recessive)-like 1 (PKHD1L1), mRNA. p8-11739614 0.0211 2 0 44154 loss CTSB Homo sapiens cathepsin B (CTSB), transcript variant 2, mRNA. p8-11739614 0.0211 2 0 44154 loss ENST00000345125 Cathepsin B precursor (EC 3.4.22.1) (Cathepsin B1) (APP secretase) (APPS) [Contains: Cathepsin B light chain; Cathepsin B heavy p8-11739614 0.0211 2 0 44154 loss ENST00000353047 Cathepsin B precursor (EC 3.4.22.1) (Cathepsin B1) (APP secretase) (APPS) [Contains: Cathepsin B light chain; Cathepsin B heavy p8-11739614 0.0211 2 0 44154 loss OR7E35P Olfactory receptor, family 7, subfamily E, member 35 pseudogene p8-12866250 0.0211 2 0 28586 loss C8orf79 chromosome 8 open reading frame 79 p9-33091997 0.0211 2 0 8125 loss B4GALT1 Homo sapiens UDP-Gal:betaGlcNAc beta 1,4- galactosyltransferase, polypeptide 1 (B4GALT1), mRNA. p9-33133734 0.0211 2 0 22501 loss B4GALT1 Homo sapiens UDP-Gal:betaGlcNAc beta 1,4- galactosyltransferase, polypeptide 1 (B4GALT1), mRNA. p9-99604289 0.0211 2 0 43592 loss C9orf156 chromosome 9 open reading frame 156 p9-99604289 0.0211 2 0 43592 loss FOXE1 forkhead box E1 (thyroid transcription factor 2) p1-151064197 0.0215 2 0 3590 gain or loss C1orf68 chromosome 1 open reading frame 68 p1-151064197 0.0215 2 0 3590 gain or loss KPRP keratinocyte proline-rich protein p1-151064197 0.0215 2 0 3590 gain or loss LCE6A late cornified envelope 6A p20-62362264 0.0215 2 0 64322 gain or loss C20orf69 chromosome 20 open reading frame 69 p20-62362264 0.0215 2 0 64322 gain or loss IQSEC3 IQ motif and Sec7 domain 3 p20-62362264 0.0215 2 0 64322 gain or loss MYT1 Myelin transcription factor 1 p20-62362264 0.0215 2 0 64322 gain or loss PCMTD2 Homo sapiens protein-L-isoaspartate (D- aspartate) O-methyltransferase domain containing 2 (PCMTD2), transcript variant 1, mRNA. p1-227356853 0.0220 2 0 26739 gain ENST00000385399 — p16-75827744 0.0220 2 0 11756 gain or loss ADAMTS18 ADAM metallopeptidase with thrombospondin type 1 motif, 18 p16-75827744 0.0220 2 0 11756 gain or loss MON1B MON1 homolog B (yeast) p2-52672295 0.0220 2 0 53151 gain or loss LOC129656 Similar to mucoepidermoid carcinoma translocated 1 isoform 2 p9-74122639 0.0220 2 0 37496 gain or loss CR619628 Full-length cDNA clone CS0DF030YH04 of Fetal brain of Homo sapiens (human) p9-74122639 0.0220 2 0 37496 gain or loss ZFAND5 Homo sapiens zinc finger, AN1-type domain 5 (ZFAND5), transcript variant c, mRNA. p9-78312920 0.0220 2 0 19194 loss GCNT1 Glucosaminyl (N-acetyl) transferase 1, core 2 (beta-1,6-N-acetylglucosaminyltransferase) p9-78340146 0.0220 2 0 22336 gain or loss LOC392352 Hypothetical LOC392352 p7-12549570 0.0221 2 0 19573 loss SCIN scinderin p10-126769820 0.0226 2 0 16839 loss CTBP2 Homo sapiens C-terminal binding protein 2 (CTBP2), transcript variant 1, mRNA. p10-126792674 0.0226 2 0 34974 loss CTBP2 Homo sapiens C-terminal binding protein 2 (CTBP2), transcript variant 1, mRNA. p16-20395870 0.0226 2 0 45066 loss ACSM2 Acyl-CoA synthetase medium-chain family member 2 p16-20395870 0.0226 2 0 45066 loss ACSM2A Homo sapiens acyl-CoA synthetase medium- chain family member 2A (ACSM2A), mRNA. p16-20395870 0.0226 2 0 45066 loss ACSM2B Homo sapiens acyl-CoA synthetase medium- chain family member 2B (ACSM2B), nuclear gene encoding mitochondrial protein, transcript p16-20395870 0.0226 2 0 45066 loss AK091978 CDNA FLJ34659 fis, clone KIDNE2018863 p16-20395870 0.0226 2 0 45066 loss ENST00000219054 Homolog of rat kidney-specific (KS) gene. [Source: Uniprot/SPTREMBL; Acc: O75202] p16-20395870 0.0226 2 0 45066 loss ENST00000329697 xenobiotic/medium-chain fatty acid:CoA ligase [Source: RefSeq_peptide; Acc: NP_872423] p16-20395870 0.0226 2 0 45066 loss ENST00000358816 CDNA FLJ34659 fis, clone KIDNE2018863. [Source: Uniprot/SPTREMBL; Acc: Q8NAW3] p16-75063352 0.0226 2 0 28695 loss CNTNAP4 Homo sapiens contactin associated protein-like 4 (CNTNAP4), transcript variant 1, mRNA. p16-75063352 0.0226 2 0 28695 loss ENST00000307431 Contactin-associated protein-like 4 precursor (Cell recognition molecule Caspr4). [Source: Uniprot/SWISSPROT; Acc: Q9C0A0] p2-132986745 0.0226 2 0 13442 gain GPR39 Homo sapiens G protein-coupled receptor 39 (GPR39), mRNA. p4-122501906 0.0226 2 0 2515 loss ENST00000334383 Orexigenic neuropeptide QRFP receptor (G- protein coupled receptor 103) (SP9155) (AQ27). [Source: Uniprot/SWISSPROT; Acc: Q96P65] p4-122501906 0.0226 2 0 2515 loss GPR103 Homo sapiens G protein-coupled receptor 103 (GPR103), mRNA. p5-76551612 0.0226 2 0 19956 loss PDE8B Homo sapiens phosphodiesterase 8B (PDE8B), transcript variant 5, mRNA. p6-150777422 0.0226 2 0 7636 loss IYD Homo sapiens iodotyrosine deiodinase (IYD), mRNA. p6-154670546 0.0226 2 0 45141 loss OPRM1 opioid receptor, mu 1 p6-154670546 0.0226 2 0 45141 loss PIP3-E Homo sapiens phosphoinositide-binding protein PIP3-E (PIP3-E), mRNA. p9-103412896 0.0226 2 0 50833 gain ENST00000361820 Glutamate [NMDA] receptor subunit 3A precursor (N-methyl-D-aspartate receptor subtype NR3A) (NMDAR-L). [Source: Uniprot/SWISSPROT p9-103412896 0.0226 2 0 50833 gain GRIN3A Homo sapiens glutamate receptor, ionotropic, N-methyl-D-aspartate 3A (GRIN3A), mRNA. p9-103412896 0.0226 2 0 50833 gain PPP3R2 Protein phosphatase 3 (formerly 2B), regulatory subunit B, beta isoform p9-74182074 0.0226 2 0 171643 loss ENST00000237937 Zinc finger A20 domain-containing protein 2 (Zinc finger protein 216). [Source: Uniprot/SWISSPROT; Acc: O76080] p9-74182074 0.0226 2 0 171643 loss TMC1 Homo sapiens transmembrane channel-like 1 (TMC1), mRNA. p9-74182074 0.0226 2 0 171643 loss ZFAND5 Homo sapiens zinc finger, AN1-type domain 5 (ZFAND5), transcript variant c, mRNA. p2-113754820 0.0228 2 0 7033 loss PAX8 Homo sapiens paired box 8 (PAX8), transcript variant PAX8A, mRNA. p1-151040530 0.0357 3 3 2838 gain or loss C1orf68 chromosome 1 open reading frame 68 p1-151040530 0.0357 3 3 2838 gain or loss KPRP keratinocyte proline-rich protein p1-151040530 0.0357 3 3 2838 gain or loss LCE1C Late cornified envelope 1C p1-151040530 0.0357 3 3 2838 gain or loss LCE6A late cornified envelope 6A

A similar comparison was performed between follicular variants of papillary carcinoma (FVPTC, n=12) and samples categorized as nodular hyperplasia (NHP, n=20). This analysis resulted in the identification of 30 significant genomic regions, which mapped to 49 known genes and/or proteins (Table 3). A third analysis aimed at papillary thyroid carcinoma (PTC, n=15) compared to NHP (n=20) resulted in the identification of 12 significant genomic regions, which mapped to 15 known genes and/or proteins (Table 4).

Table 4. Top 30 genomic regions that differentiate Follicular Variant of Papillary Carcinoma (FVPTC) from Nodular Hyperplasia (NHP). A significance filter of p<0.05 was used, followed by ranking in descending order. PLINK features in FVPTC (n=12) were compared to NHP (n=20). The 30 genomic regions that differentiate FVPTC from NHP are mapped to 29 known genes and or proteins.

Genomic Region, chromosome number and start of genomic position of a given PLINK feature; P, p-value of group comparison for a given PLINK feature, Total FC with Feature, number of FC samples harboring a given PLINK feature; Total FA with Feature, number of FA samples harboring a given PLINK feature; Genomic Size, size of a given PLINK feature; Gene, name of known genes mapped to a given genomic region. Single genomic regions often coded for multiple genes and/or proteins. Similarly, several genomic regions (or significant PLINK features) often mapped next to one another and consequently mapped to the same gene or genes.

TABLE 4 Total Total FVPTC NHP with with Nature of Genomic Feature Feature Genomic Genomic Region P n = 12 n = 20 Size Feature Gene Description p12-33192661 0.0035 5 0 5969 loss p7-143664773 0.0038 5 0 33096 loss ARHGEF5 Rho guanine nucleotide exchange factor (GEF) 5 p7-143664773 0.0038 5 0 33096 loss LOC728377 similar to rho guanine nucleotide exchange factor 5 p7-143664773 0.0038 5 0 33096 loss OR2A20P olfactory receptor, family 2, subfamily A, member 20 pseudogene p7-143664773 0.0038 5 0 33096 loss OR2A5 olfactory receptor, family 2, subfamily A, member 5 p7-143664773 0.0038 5 0 33096 loss OR2A9P olfactory receptor, family 2, subfamily A, member 9 pseudogene p7-143664773 0.0038 5 0 33096 loss OR2A42 Olfactory receptor, family 2, subfamily A, member 42 p19-20462472 0.0132 4 0 10799 loss ZNF486 zinc finger protein 486 p19-20462472 0.0132 4 0 10799 loss ZNF626 zinc finger protein 626 p19-20462472 0.0132 4 0 10799 loss ZNF826 zinc finger protein 826 p12-96414379 0.0133 4 0 26086 loss RMST rhabdomyosarcoma 2 associated transcript (non-protein coding) p12-96383344 0.0140 4 0 22221 loss RMST rhabdomyosarcoma 2 associated transcript (non-protein coding) p2-52634819 0.0196 7 3 216 gain or loss p7-143658498 0.0197 5 1 6275 loss OR2A20P olfactory receptor, family 2, subfamily A, member 20 pseudogene p7-143658498 0.0197 5 1 6275 loss OR2A5 olfactory receptor, family 2, subfamily A, member 5 p7-143658498 0.0197 5 1 6275 loss OR2A9P olfactory receptor, family 2, subfamily A, member 9 pseudogene p6-79044205 0.0264 9 6 7686 gain or loss p6-79051930 0.0264 9 6 18779 gain or loss p6-79070723 0.0264 9 6 9530 gain or loss p11-4926841 0.0271 9 6 5429 gain or OR51A4 Olfactory receptor, family 51, loss subfamily A, member 4 p11-4926841 0.0271 9 6 5429 gain or OR51A2 Olfactory receptor, family 51, loss subfamily A, member 2 p11-4926841 0.0271 9 6 5429 gain or MMP26 matrix metallopeptidase 26 loss p13-22512071 0.0419 3 0 15105 gain or loss p7-154683208 0.0425 3 0 2440 loss INSIG1 insulin induced gene 1 p7-11567280 0.0426 3 0 12938 gain or THSD7A thrombospondin, type I, domain loss containing 7A p14-74810461 0.0433 3 0 25014 gain or FOS v-fos FBJ murine osteosarcoma viral loss oncogene homolog p14-74810461 0.0433 3 0 25014 gain or LOC341912 similar to developmental loss pluripotency associated 5; embryonal stem cell specific gene 1 p14-74810461 0.0433 3 0 25014 gain or LOC731223 hypothetical LOC731223 loss p14-74810461 0.0433 3 0 25014 gain or LOC646701 Similar to developmental loss pluripotency associated 5 p19-20473271 0.0433 3 0 11969 loss ZNF486 zinc finger protein 486 p19-20473271 0.0433 3 0 11969 loss ZNF626 zinc finger protein 626 p19-20473271 0.0433 3 0 11969 loss ZNF826 zinc finger protein 826 p11-18236772 0.0433 3 0 1028 gain p19-20388034 0.0433 3 0 2238 loss ZNF486 zinc finger protein 486 p19-20388034 0.0433 3 0 2238 loss ZNF626 zinc finger protein 626 p19-20388034 0.0433 3 0 2238 loss ZNF826 zinc finger protein 826 p21-33181962 0.0433 3 0 18291 gain or loss p8-2277817 0.0433 3 0 13687 gain p12-96362816 0.0436 3 0 20529 loss RMST rhabdomyosarcoma 2 associated transcript (non-protein coding) p12-96405565 0.0436 3 0 8815 loss RMST rhabdomyosarcoma 2 associated transcript (non-protein coding) p12-96440465 0.0436 3 0 64651 loss ENST00000384854 hsa-mir-135a-2 p12-96440465 0.0436 3 0 64651 loss RMST rhabdomyosarcoma 2 associated transcript (non-protein coding) p19-20443816 0.0437 3 0 518 loss ZNF486 zinc finger protein 486 p19-20443816 0.0437 3 0 518 loss ZNF626 zinc finger protein 626 p19-20443816 0.0437 3 0 518 loss ZNF826 zinc finger protein 826 p19-20450389 0.0437 3 0 12084 loss ZNF486 zinc finger protein 486 p19-20450389 0.0437 3 0 12084 loss ZNF626 zinc finger protein 626 p19-20450389 0.0437 3 0 12084 loss ZNF826 zinc finger protein 826 p19-20450389 0.0437 3 0 12084 loss ZNF253 Zinc finger protein 253 p3-131232884 0.0438 3 0 3730 gain LOC441268 hypothetical gene supported by BC044942 p3-131232884 0.0438 3 0 3730 gain OR7E37P olfactory receptor, family 7, subfamily E, member 37 pseudogene p1-72532085 0.0444 3 0 3168 loss LOC100132353 similar to GDP dissociation inhibitor 2 p1-72532085 0.0444 3 0 3168 loss LOC642098 hypothetical protein LOC642098 p10-47114754 0.0447 3 0 36917 gain ANTXRL anthrax toxin receptor-like p10-47114754 0.0447 3 0 36917 gain BMS1P5 BMS1 pseudogene 5 p10-47114754 0.0447 3 0 36917 gain CTGLF6 centaurin, gamma-like family, member 6 p10-47114754 0.0447 3 0 36917 gain CTGLF7 centaurin, gamma-like family, member 7 p10-47114754 0.0447 3 0 36917 gain LOC642826 hypothetical LOC642826 p2-129603279 0.0451 3 0 6387 loss p7-143697869 0.0452 3 0 36182 loss ARHGEF5 Rho guanine nucleotide exchange factor (GEF) 5 p7-143697869 0.0452 3 0 36182 loss LOC728377 similar to rho guanine nucleotide exchange factor 5 p7-143697869 0.0452 3 0 36182 loss NOBOX NOBOX oogenesis homeobox

Table 5. Top 12 markers that differentiate Papillary Thyroid Carcinoma (PTC) from Nodular Hyperplasia (NHP). Markers are ranked based on p-value (p<0.05). DNA copy number in PTC (n=15) was compared to NHP (n=20). The 12 markers that differentiate PTC from NHP are mapped to 15 known genes and/or proteins.

Genomic Region, chromosome number and start of genomic position of a given PLINK feature; P, p-value of group comparison for a given PLINK feature, Total FC with Feature, number of FC samples harboring a given PLINK feature; Total FA with Feature, number of FA samples harboring a given PLINK feature; Genomic Size, size of a given PLINK feature; Gene, name of known genes mapped to a given genomic region. Single genomic regions often coded for multiple genes and/or proteins. Similarly, several genomic regions (or significant PLINK features) often mapped next to one another and consequently mapped to the same gene or genes.

TABLE 5 Total Total PTC NHP with with Nature of Genomic Feature Feature Genomic Genomic Region P n = 15 n = 20 Size Feature Gene Description p13-56656378 0.0198 10 5 16724 gain or loss XM_001132965 — p13-56656378 0.0198 10 5 16724 gain or loss BC043197 CDNA clone IMAGE: 5288938 p12-96389719 0.0091 5 0 14666 loss AK129935 CDNA FLJ26425 fis, clone KDN01013 p12-96368115 0.0262 4 0 21605 loss AK129935 CDNA FLJ26425 fis, clone KDN01013 p12-96404385 0.0262 4 0 17276 loss AK129935 CDNA FLJ26425 fis, clone KDN01013 p6-209744 0.0263 4 0 388 gain or loss DUSP22 dual specificity phosphatase 22 p12-38887975 0.0091 5 0 37458 loss LRRK2 Homo sapiens leucine-rich repeat kinase 2 p12-38925433 0.0262 4 0 125604 loss LRRK2 Homo sapiens leucine-rich repeat kinase 2 p12-96440000 0.0262 4 0 57442 loss ENST00000384854 hsa-mir-135a-2 p12-38884795 0.0265 4 0 3181 loss LRRK2 leucine-rich repeat kinase 2 p1-246798656 0.0276 0 6 5403 neutral OR2T3 Olfactory receptor, family 2, subfamily T, member 3 p1-246798656 0.0276 0 6 5403 neutral OR2T34 Olfactory receptor, family 2, subfamily T, member 34 p12-96368115 0.0262 4 0 21605 loss RMST Rhabdomyosarcoma 2 associated transcript (non-coding RNA) p12-96440000 0.0262 4 0 57442 loss RMST Rhabdomyosarcoma 2 associated transcript (non-coding RNA) p4-25111155 0.0260 4 0 3546 gain or loss LOC645433 Similar to myo-inositol 1- phosphate synthase A1 p11-93331967 0.0263 4 0 5628 gain or loss ENST00000398221 Uncharacterized protein ENSP00000381277

The fourth analysis grouped data from all available malignant samples in this cohort (n=31) and compared them to a group of all available benign samples (n=55). This analysis resulted in the identification of 250 significant genomic regions, which mapped to 561 known genes and/or proteins (Table 5).

Table 6. Top 100 markers that differentiate malignant (M) from Benign (B). Markers are ranked based on p-value (p<0.05). DNA copy number in M (n=31) was compared to B (n=55). A total of 250 markers differentiate malignant from benign, and these map to 473 known genes and/or proteins.

Genomic Region, chromosome number and start of genomic position of a given PLINK feature; P, p-value of group comparison for a given PLINK feature, Total FC with Feature, number of FC samples harboring a given PLINK feature; Total FA with Feature, number of FA samples harboring a given PLINK feature; Genomic Size, size of a given PLINK feature; Gene, name of known genes mapped to a given genomic region. Single genomic regions often coded for multiple genes and/or proteins. Similarly, several genomic regions (or significant PLINK features) often mapped next to one another and consequently mapped to the same gene or genes.

TABLE 6 Total Total Malignant Benign with with Nature of Genomic Feature Feature Genomic Region P n = 31 n = 55 Genomic Size Feature Gene Description p12-96389719 0.0001 11 2 14666 loss RMST rhabdomyosarcoma 2 associated transcript (non-protein coding) p12-96488083 0.0003 8 0 2330 loss p12-96414379 0.0004 10 2 7282 loss RMST rhabdomyosarcoma 2 associated transcript (non-protein coding) p12-96383862 0.0005 10 2 5858 loss RMST rhabdomyosarcoma 2 associated transcript (non-protein coding) p12-96404385 0.0005 10 2 1180 loss RMST rhabdomyosarcoma 2 associated transcript (non-protein coding) p12-96490413 0.0006 7 0 7029 loss p12-96479234 0.0010 8 1 8849 loss RMST rhabdomyosarcoma 2 associated transcript (non-protein coding) p13-56661136 0.0010 19 13 205 gain or loss p12-96421661 0.0011 9 2 12107 loss RMST rhabdomyosarcoma 2 associated transcript (non-protein coding) p12-96440000 0.0011 9 2 465 loss RMST rhabdomyosarcoma 2 associated transcript (non-protein coding) p12-96383344 0.0013 10 3 518 loss RMST rhabdomyosarcoma 2 associated transcript (non-protein coding) p12-96405565 0.0013 9 2 8815 loss RMST rhabdomyosarcoma 2 associated transcript (non-protein coding) p13-56661340 0.0014 19 14 3449 gain or loss p12-96370419 0.0015 9 2 8502 loss RMST rhabdomyosarcoma 2 associated transcript (non-protein coding) p13-56664788 0.0027 19 15 8314 gain or loss p8-5586182 0.0030 7 1 5115 gain or loss p12-96433768 0.0034 8 2 6233 loss RMST rhabdomyosarcoma 2 associated transcript (non-protein coding) p12-96440465 0.0037 8 2 38769 loss RMST rhabdomyosarcoma 2 associated transcript (non-protein coding) p12-96378920 0.0040 9 3 4425 loss RMST rhabdomyosarcoma 2 associated transcript (non-protein coding) p12-52648461 0.0046 5 0 14071 gain or loss HOXC11 homeobox C11 p13-56656615 0.0047 17 13 4522 gain or loss p12-96497442 0.0049 5 0 7674 loss p12-38887975 0.0051 5 0 37458 loss LRRK2 leucine-rich repeat kinase 2 p13-56673102 0.0058 18 15 104 gain or loss p2-113690667 0.0071 6 1 2336 loss PAX8 paired box 8 p8-5591297 0.0085 6 1 607 loss p2-113735599 0.0090 7 2 6244 loss LOC654433 hypothetical LOC654433 p2-113735599 0.0090 7 2 6244 loss PAX8 paired box 8 p12-96368115 0.0099 7 2 2305 loss RMST rhabdomyosarcoma 2 associated transcript (non-protein coding) p13-56674559 0.0102 16 13 1799 gain or loss BC043197 CDNA clone IMAGE: 5288938 p13-56656378 0.0103 16 13 238 gain or loss p12-52662532 0.0137 4 0 37896 gain or loss HOXC10 homeobox C10 p12-52662532 0.0137 4 0 37896 gain or loss HOXC4 homeobox C4 p12-52662532 0.0137 4 0 37896 gain or loss HOXC5 homeobox C5 p12-52662532 0.0137 4 0 37896 gain or loss HOXC6 homeobox C6 p12-52662532 0.0137 4 0 37896 gain or loss HOXC8 homeobox C8 p12-52662532 0.0137 4 0 37896 gain or loss HOXC9 homeobox C9 p9-597816 0.0141 4 0 9745 gain or loss KANK1 KN motif and ankyrin repeat domains 1 p9-607804 0.0141 4 0 11383 gain or loss KANK1 KN motif and ankyrin repeat domains 1 p9-628296 0.0141 4 0 852 gain or loss KANK1 KN motif and ankyrin repeat domains 1 p21-33193147 0.0144 4 0 7106 gain or loss p9-44183418 0.0144 4 0 1447 gain or loss LOC728832 Hypothetical protein LOC728832 p9-44183418 0.0144 4 0 1447 gain or loss LOC728903 Hypothetical protein LOC728903 p9-44183418 0.0144 4 0 1447 gain or loss RP11-262H14.4 Hypothetical locus MGC21881 p7-116093085 0.0145 4 0 8085 loss MET met proto-oncogene (hepatocyte growth factor receptor) p11-70946812 0.0146 4 0 2081 gain p12-16609928 0.0146 4 0 19961 loss LMO3 LIM domain only 3 (rhombotin-like 2) p12-16609928 0.0146 4 0 19961 loss MGST1 microsomal glutathione S-transferase 1 p4-25111155 0.0146 4 0 3546 gain or loss p12-38862050 0.0148 4 0 4658 loss p1-56808636 0.0148 4 0 6288 loss PPAP2B phosphatidic acid phosphatase type 2B p1-62528204 0.0148 4 0 9846 gain or loss KANK4 KN motif and ankyrin repeat domains 4 p1-108879423 0.0149 4 0 12448 loss p12-38925433 0.0149 4 0 125604 loss LRRK2 leucine-rich repeat kinase 2 p1-227356853 0.0149 4 0 17723 gain or loss p2-129605867 0.0149 4 0 3799 loss p3-11615927 0.0149 4 0 9022 gain or loss LOC100133039 hypothetical protein LOC100133039 p3-11615927 0.0149 4 0 9022 gain or loss VGLL4 vestigial like 4 (Drosophila) p6-150773841 0.0151 4 0 2109 gain or loss IYD iodotyrosine deiodinase p8-5591904 0.0153 4 0 2661 loss p13-56647720 0.0153 4 0 8540 gain or loss p2-85978677 0.0153 4 0 12230 loss ST3GAL5 ST3 beta-galactoside alpha-2,3- sialyltransferase 5 p5-6625206 0.0153 4 0 12593 gain or loss LOC255167 hypothetical LOC255167 p5-6625206 0.0153 4 0 12593 gain or loss NSUN2 NOL1/NOP2/Sun domain family, member 2 p12-52621535 0.0154 4 0 26927 gain or loss FLJ41747 hypothetical gene supported by AK123741 p12-52621535 0.0154 4 0 26927 gain or loss HOTAIR hox transcript antisense RNA (non-protein coding) p12-52621535 0.0154 4 0 26927 gain or loss HOXC12 homeobox C12 p12-52621535 0.0154 4 0 26927 gain or loss HOXC13 homeobox C13 p1-236101606 0.0154 4 0 119 gain or loss ZP4 zona pellucida glycoprotein 4 p9-78315296 0.0154 4 0 816 gain or loss p12-96505116 0.0157 4 0 1112 loss p12-16629889 0.0158 4 0 11001 loss LMO3 LIM domain only 3 (rhombotin-like 2) p12-16629889 0.0158 4 0 11001 loss MGST1 microsomal glutathione S-transferase 1 p12-38884795 0.0165 4 0 3181 loss LRRK2 leucine-rich repeat kinase 2 p13-56673206 0.0189 17 15 1353 gain or loss p19-20388034 0.0206 5 1 2238 loss ZNF486 zinc finger protein 486 p19-20388034 0.0206 5 1 2238 loss ZNF626 zinc finger protein 626 p19-20388034 0.0206 5 1 2238 loss ZNF826 zinc finger protein 826 p2-113677467 0.0209 5 1 6033 loss PAX8 paired box 8 p2-113683546 0.0209 5 1 7122 loss PAX8 paired box 8 p9-113461430 0.0218 5 1 394 gain or loss DNAJC25 DnaJ (Hsp40) homolog, subfamily C, member 25 p9-113461430 0.0218 5 1 394 gain or loss GNG10 guanine nucleotide binding protein (G protein), gamma 10 p9-113461430 0.0218 5 1 394 gain or loss LOC552891 hypothetical protein LOC552891 p2-113741843 0.0225 6 2 12977 loss PAX8 paired box 8 p2-113693002 0.0231 6 2 42598 loss LOC654433 hypothetical LOC654433 p2-113693002 0.0231 6 2 42598 loss PAX8 paired box 8 p6-81341665 0.0233 6 2 4577 gain or loss p12-96362816 0.0236 6 2 5300 loss RMST rhabdomyosarcoma 2 associated transcript (non-protein coding) p9-113455994 0.0239 6 2 630 gain or loss DNAJC25 DnaJ (Hsp40) homolog, subfamily C, member 25 p9-113455994 0.0239 6 2 630 gain or loss GNG10 guanine nucleotide binding protein (G protein), gamma 10 p9-113455994 0.0239 6 2 630 gain or loss LOC552891 hypothetical protein LOC552891 p13-56656259 0.0301 15 13 120 gain or loss p9-113450346 0.0329 7 3 2694 gain or loss DNAJC25 DnaJ (Hsp40) homolog, subfamily C, member 25 p9-113450346 0.0329 7 3 2694 gain or loss GNG10 guanine nucleotide binding protein (G protein), gamma 10 p9-113450346 0.0329 7 3 2694 gain or loss LOC552891 hypothetical protein LOC552891 p7-154683208 0.0422 3 0 2440 loss INSIG1 insulin induced gene 1 p3-13008860 0.0425 3 0 11432 gain or loss IQSEC1 IQ motif and Sec7 domain 1 p6-162853395 0.0425 3 0 232 gain or loss PARK2 Parkinson disease (autosomal recessive, juvenile) 2, parkin p9-607561 0.0425 3 0 244 gain or loss KANK1 KN motif and ankyrin repeat domains 1 p3-11624949 0.0426 3 0 4059 gain or loss LOC100133039 hypothetical protein LOC100133039 p3-11624949 0.0426 3 0 4059 gain or loss VGLL4 vestigial like 4 (Drosophila) p9-131076533 0.0426 3 0 4458 gain or loss p12-52700428 0.0428 3 0 30916 gain or loss HOXC4 homeobox C4 p12-52700428 0.0428 3 0 30916 gain or loss HOXC5 homeobox C5 p12-52700428 0.0428 3 0 30916 gain or loss HOXC6 homeobox C6 p16-75066912 0.0428 3 0 25135 loss CNTNAP4 contactin associated protein-like 4 p2-71614062 0.0428 3 0 19339 gain or loss DYSF dysferlin, limb girdle muscular dystrophy 2B (autosomal recessive) p1-62527052 0.0429 3 0 1153 gain KANK4 KN motif and ankyrin repeat domains 4 p4-21102836 0.0429 3 0 11336 gain KCNIP4 Kv channel interacting protein 4 p4-21102836 0.0429 3 0 11336 gain UM9(5) non-coding transcript UM9(5) p11-18236772 0.0429 3 0 1028 gain p21-33181962 0.0429 3 0 11186 gain or loss p8-2284699 0.0429 3 0 6805 gain p5-80116897 0.0429 3 0 30939 loss MSH3 mutS homolog 3 (E. coli) p15-63326096 0.0430 3 0 910 gain or loss PARP16 poly (ADP-ribose) polymerase family, member 16 p9-91515481 0.0430 3 0 17130 gain or loss FLJ42342 hypothetical gene supported by AK124333 p9-91515481 0.0430 3 0 17130 gain or loss LOC100130355 hypothetical protein LOC100130355 p1-227374576 0.0430 3 0 9016 gain or loss p12-15722847 0.0431 3 0 4809 loss EPS8 epidermal growth factor receptor pathway substrate 8 p15-92603904 0.0431 3 0 5908 gain or loss MCTP2 multiple C2 domains, transmembrane 2 p8-68710001 0.0431 3 0 2748 gain or loss CPA6 carboxypeptidase A6 p1-161631533 0.0431 3 0 7122 gain NUF2 NUF2, NDC80 kinetochore complex component, homolog (S. cerevisiae) p13-68149638 0.0432 3 0 7367 loss p2-216935552 0.0432 3 0 18212 loss MARCH4 membrane-associated ring finger (C3HC4) 4 p13-23691490 0.0432 3 0 18121 gain or loss SPATA13 spermatogenesis associated 13 p7-12549570 0.0433 3 0 11558 loss SCIN scinderin p4-35052424 0.0433 3 0 7008 loss p8-2330925 0.0433 3 0 6766 gain or loss p9-139771275 0.0433 3 0 1084 gain or loss EHMT1 euchromatic histone-lysine N- methyltransferase 1 p9-139771275 0.0433 3 0 1084 gain or loss FLJ40292 hypothetical LOC643210 p9-587599 0.0433 3 0 5293 gain or loss KANK1 KN motif and ankyrin repeat domains 1 p9-597295 0.0433 3 0 522 gain or loss KANK1 KN motif and ankyrin repeat domains 1 p9-629148 0.0433 3 0 18532 gain or loss KANK1 KN motif and ankyrin repeat domains 1 p13-56647025 0.0433 3 0 696 gain or loss p16-20407950 0.0434 3 0 20279 loss LOC100129488 hypothetical protein LOC100129488 p14-39946066 0.0434 3 0 13352 gain or loss p7-11567280 0.0434 3 0 12938 gain or loss THSD7A thrombospondin, type I, domain containing 7A p1-157727198 0.0435 3 0 8540 gain or loss OR10J5 olfactory receptor, family 10, subfamily J, member 5 p14-40746554 0.0435 3 0 22110 gain or loss p2-2568467 0.0435 3 0 18835 loss p6-164030494 0.0435 3 0 30959 gain or loss p1-227341339 0.0435 3 0 15515 gain or loss p3-29318339 0.0435 3 0 13729 gain or loss RBMS3 RNA binding motif, single stranded interacting protein p1-108891871 0.0435 3 0 18072 loss FAM102B family with sequence similarity 102, member B p12-13261503 0.0435 3 0 6739 gain or loss EMP1 epithelial membrane protein 1 p12-13261503 0.0435 3 0 6739 gain or loss KBTBD2 kelch repeat and BTB (POZ) domain containing 2 p12-24994910 0.0435 3 0 2258 gain or loss p12-51229951 0.0435 3 0 2519 loss KRT71 keratin 71 p12-51229951 0.0435 3 0 2519 loss KRT8 keratin 8 p16-7644204 0.0435 3 0 10605 loss A2BP1 ataxin 2-binding protein 1 p21-33200253 0.0435 3 0 12550 gain or loss LOC100134323 hypothetical protein LOC100134323 p13-23552228 0.0436 3 0 36498 gain or loss LOC100128337 similar to RanBP7/importin 7 p13-23552228 0.0436 3 0 36498 gain or loss SPATA13 spermatogenesis associated 13 p11-12241283 0.0436 3 0 7562 gain or loss MICAL2 microtubule associated monoxygenase, calponin and LIM domain containing 2 p11-12241283 0.0436 3 0 7562 gain or loss MICALCL MICAL C-terminal like p12-52615175 0.0436 3 0 6361 gain or loss HOXC13 homeobox C13 p15-29608176 0.0436 3 0 20065 loss LOC283713 hypothetical protein LOC283713 p15-29608176 0.0436 3 0 20065 loss OTUD7A OTU domain containing 7A p1-62538050 0.0436 3 0 22662 gain or loss KANK4 KN motif and ankyrin repeat domains 4 p2-129609666 0.0436 3 0 10699 loss p8-5578648 0.0436 3 0 2893 gain or loss p1-117384821 0.0436 3 0 18322 gain TTF2 transcription termination factor, RNA polymerase II p3-11608587 0.0436 3 0 7341 gain or loss LOC100133039 hypothetical protein LOC100133039 p3-11608587 0.0436 3 0 7341 gain or loss VGLL4 vestigial like 4 (Drosophila) p3-131453791 0.0436 3 0 9588 gain or loss LOC100131335 hypothetical protein LOC100131335 p3-131453791 0.0436 3 0 9588 gain or loss LOC646300 similar to mCG140660 p8-75216096 0.0436 3 0 14571 gain or loss p10-121786674 0.0437 3 0 30649 gain or loss XM_940278 p3-173825690 0.0437 3 0 7800 gain or loss AADACL1 arylacetamide deacetylase-like 1 p12-6185521 0.0437 3 0 23870 gain or loss CD9 CD9 molecule p8-91713451 0.0437 3 0 3969 gain or loss TMEM64 transmembrane protein 64 p9-5431686 0.0437 3 0 20044 gain or loss LOC728903 Hypothetical protein LOC728903 p9-5431686 0.0437 3 0 20044 gain or loss RP11-262H14.4 Hypothetical locus MGC21881 p2-85990907 0.0437 3 0 6080 loss ST3GAL5 ST3 beta-galactoside alpha-2,3- sialyltransferase 5 p1-95106416 0.0438 3 0 35371 gain or loss CNN3 calponin 3, acidic p1-95106416 0.0438 3 0 35371 gain or loss SLC44A3 solute carrier family 44, member 3 p14-55558147 0.0438 3 0 8063 gain or loss p14-93984925 0.0438 3 0 2952 gain SERPINA11 serpin peptidase inhibitor, clade A (alpha-1 antiproteinase, antitrypsin), member 11 p14-94013799 0.0438 3 0 326 gain or loss p11-58705578 0.0439 3 0 34490 loss DTX4 deltex 4 homolog (Drosophila) p11-58705578 0.0439 3 0 34490 loss MPEG1 macrophage expressed gene 1 p11-58705578 0.0439 3 0 34490 loss OR5A2 olfactory receptor, family 5, subfamily A, member 2 p11-58705578 0.0439 3 0 34490 loss OR5B21 olfactory receptor, family 5, subfamily B, member 21 p11-58745622 0.0439 3 0 22110 loss LOC341112 similar to hCG40922 p11-58745622 0.0439 3 0 22110 loss OR5A2 olfactory receptor, family 5, subfamily A, member 2 p11-58745622 0.0439 3 0 22110 loss OR5B21 olfactory receptor, family 5, subfamily B, member 21 p20-905881 0.0439 3 0 3926 gain or loss RSPO4 R-spondin family, member 4 p14-63007451 0.0439 3 0 35826 gain or loss PPP2R5E protein phosphatase 2, regulatory subunit B′, epsilon isoform p9-122380439 0.0439 3 0 13475 gain or loss CDK5RAP2 CDK5 regulatory subunit associated protein 2 p2-52649619 0.0440 3 0 11453 loss p3-171691229 0.0440 3 0 16682 gain SLC7A14 solute carrier family 7 (cationic amino acid transporter, y+ system), member 14 p6-150775950 0.0440 3 0 1472 loss IYD iodotyrosine deiodinase p1-86755723 0.0440 3 0 9982 gain or loss CLCA4 chloride channel, calcium activated, family member 4 p4-107043531 0.0440 3 0 19967 loss NPNT nephronectin p4-186393053 0.0440 3 0 63977 loss SNX25 sorting nexin 25 p8-11753883 0.0440 3 0 29885 loss CTSB cathepsin B p9-33133734 0.0440 3 0 13115 loss B4GALT1 UDP-Gal:betaGlcNAc beta 1,4- galactosyltransferase, polypeptide 1 p2-52672295 0.0441 3 0 3711 gain or loss p6-4610830 0.0441 3 0 1012 gain or loss KU-MEL-3 KU-MEL-3 p8-1239382 0.0441 3 0 5972 gain or loss C8orf68 chromosome 8 open reading frame 68 p8-1239382 0.0441 3 0 5972 gain or loss LOC286083 hypothetical protein LOC286083 p8-1239382 0.0441 3 0 5972 gain or loss LOC401442 hypothetical gene supported by BC028401 p2-129603279 0.0441 3 0 2589 loss p6-33076834 0.0441 3 0 19840 gain or loss HLA-DOA major histocompatibility complex, class II, DO alpha p10-116380465 0.0442 3 0 36406 loss ABLIM1 actin binding LIM protein 1 p1-120269948 0.0442 3 0 8226 loss LOC100132495 hypothetical protein LOC100132495 p1-120269948 0.0442 3 0 8226 loss NOTCH2 Notch homolog 2 (Drosophila) p1-120269948 0.0442 3 0 8226 loss NOTCH2NL Notch homolog 2 (Drosophila) N-terminal like p14-98545336 0.0442 3 0 2781 loss p6-109458038 0.0442 3 0 59225 loss C6orf182 chromosome 6 open reading frame 182 p6-109458038 0.0442 3 0 59225 loss SESN1 sestrin 1 p10-123980234 0.0442 3 0 16865 gain or loss TACC2 transforming, acidic coiled-coil containing protein 2 p1-37621380 0.0442 3 0 31917 gain or loss p19-36628377 0.0442 3 0 550 gain or loss p10-112626043 0.0442 3 0 4001 loss PDCD4 programmed cell death 4 (neoplastic transformation inhibitor) p11-116861102 0.0442 3 0 30217 gain or loss DSCAML1 Down syndrome cell adhesion molecule like 1 p4-13371215 0.0442 3 0 15869 loss p4-25110555 0.0442 3 0 601 gain or loss p18-54038970 0.0443 3 0 34290 loss NEDD4L neural precursor cell expressed, developmentally down-regulated 4-like p18-54095624 0.0443 3 0 1147 loss NEDD4L neural precursor cell expressed, developmentally down-regulated 4-like p9-14223302 0.0443 3 0 36820 loss NFIB nuclear factor I/B p22-22374488 0.0443 3 0 35676 gain or loss IGLL3 immunoglobulin lambda-like polypeptide 3 p22-22374488 0.0443 3 0 35676 gain or loss LOC100128388 similar to hCG39998 p22-22374488 0.0443 3 0 35676 gain or loss LOC51233 hypothetical protein LOC51233 p22-22374488 0.0443 3 0 35676 gain or loss LOC91316 similar to bK246H3.1 (immunoglobulin lambda-like polypeptide 1, pre-B-cell specific) p22-22374488 0.0443 3 0 35676 gain or loss SMARCB1 SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily b, member 1 p8-53432034 0.0443 3 0 8324 gain or loss ST18 suppression of tumorigenicity 18 (breast carcinoma) (zinc finger protein) p12-113643987 0.0443 3 0 2466 gain or loss p20-1288428 0.0443 3 0 45438 loss FKBP1A FK506 binding protein 1A, 12 kDa p20-1288428 0.0443 3 0 45438 loss FKBP1C FK506 binding protein 1C p11-70946167 0.0444 3 0 646 gain p3-12667014 0.0444 3 0 6521 loss RAF1 v-raf-1 murine leukemia viral oncogene homolog 1 p3-12667014 0.0444 3 0 6521 loss RAP1A RAP1A, member of RAS oncogene family p12-21539247 0.0444 3 0 31057 gain or loss GOLT1B golgi transport 1 homolog B (S. cerevisiae) p12-21539247 0.0444 3 0 31057 gain or loss RECQL RecQ protein-like (DNA helicase Q1-like) p16-82867177 0.0444 3 0 546 gain or loss KCNG4 potassium voltage-gated channel, subfamily G, member 4 p13-94697355 0.0444 3 0 44378 gain or loss ABCC4 ATP-binding cassette, sub-family C (CFTR/MRP), member 4 p19-57552006 0.0444 3 0 13425 gain LOC400713 zinc finger-like p19-57552006 0.0444 3 0 13425 gain ZNF610 zinc finger protein 610 p9-79399424 0.0444 3 0 34652 gain or loss GNA14 guanine nucleotide binding protein (G protein), alpha 14 p3-149874647 0.0444 3 0 36159 gain or loss AGTR1 angiotensin II receptor, type 1 p3-147728628 0.0444 3 0 7482 gain or loss PLSCR1 phospholipid scramblase 1 p1-108875416 0.0445 3 0 4008 loss NBPF22P neuroblastoma breakpoint family, member 22 (pseudogene) p1-108875416 0.0445 3 0 4008 loss NBPF4 neuroblastoma breakpoint family, member 4 p6-18569138 0.0445 3 0 13607 gain or loss DDX18 DEAD (Asp-Glu-Ala-Asp) box polypeptide 18 p6-18569138 0.0445 3 0 13607 gain or loss DEK DEK oncogene p6-18569138 0.0445 3 0 13607 gain or loss RNF144B ring finger 144B p7-7985006 0.0445 3 0 11069 loss GLCCI1 glucocorticoid induced transcript 1 p7-7985006 0.0445 3 0 11069 loss LOC100131104 similar to Peptidylprolyl isomerase (cyclophilin)-like 4 p7-7985006 0.0445 3 0 11069 loss LOC100131104 similar to Peptidylprolyl isomerase (cyclophilin)-like 4 p7-7985006 0.0445 3 0 11069 loss LOC100131104 similar to Peptidylprolyl isomerase (cyclophilin)-like 4 p7-7985006 0.0445 3 0 11069 loss tcag7.903 hypothetical protein LOC729852 p13-22530993 0.0445 3 0 24526 gain or loss p21-34328947 0.0445 3 0 12360 gain or loss MRPS6 mitochondrial ribosomal protein S6 p21-34328947 0.0445 3 0 12360 gain or loss SLC5A3 solute carrier family 5 (sodium/myo-inositol cotransporter), member 3 p12-64519077 0.0445 3 0 108038 loss HMGA2 high mobility group AT-hook 2 p12-64519077 0.0445 3 0 108038 loss LOC100129940 hypothetical LOC100129940 p3-131232884 0.0445 3 0 3730 gain LOC441268 hypothetical gene supported by BC044942 p3-131232884 0.0445 3 0 3730 gain OR7E37P olfactory receptor, family 7, subfamily E, member 37 pseudogene p3-71848472 0.0445 3 0 4362 loss EIF4E3 eukaryotic translation initiation factor 4E family member 3 p9-85053935 0.0445 3 0 12519 loss FRMD3 FERM domain containing 3 p2-105179169 0.0446 3 0 3648 gain GPR45 G protein-coupled receptor 45 p2-105179169 0.0446 3 0 3648 gain LOC100133048 hypothetical protein LOC100133048 p11-7467081 0.0446 3 0 18216 loss OLFML1 olfactomedin-like 1 p11-7563080 0.0446 3 0 15364 loss PPFIBP2 PTPRF interacting protein, binding protein 2 (liprin beta 2) p7-80519195 0.0446 3 0 31275 gain or loss p9-20579688 0.0446 3 0 36188 loss MLLT3 myeloid/lymphoid or mixed-lineage leukemia (trithorax homolog, Drosophila); translocated to, 3 p12-39051037 0.0446 3 0 4326 loss LRRK2 Leucine-rich repeat kinase 2 p5-180380777 0.0446 3 0 40090 gain or loss BTNL9 butyrophilin-like 9 p10-116098703 0.0446 3 0 23751 loss AFAP1L2 actin filament associated protein 1-like 2 p5-164373590 0.0446 3 0 4604 gain or loss p9-99604289 0.0446 3 0 20714 gain or loss C9orf156 chromosome 9 open reading frame 156 p9-99604289 0.0446 3 0 20714 gain or loss FOXE1 forkhead box E1 (thyroid transcription factor 2) p11-92241464 0.0447 3 0 21651 gain FAT3 FAT tumor suppressor homolog 3 (Drosophila) p1-156226022 0.0447 3 0 9584 loss KIRREL kin of IRRE like (Drosophila) p4-25114701 0.0447 3 0 7128 gain or loss p4-88066723 0.0447 3 0 30311 gain or loss AFF1 AF4/FMR2 family, member 1 p4-88066723 0.0447 3 0 30311 gain or loss C4orf36 chromosome 4 open reading frame 36 p4-88066723 0.0447 3 0 30311 gain or loss GPSN2 glycoprotein, synaptic 2 p4-88066723 0.0447 3 0 30311 gain or loss LOC728530 hypothetical LOC728530 p4-88066723 0.0447 3 0 30311 gain or loss LOC728530 hypothetical LOC728530 p5-131602022 0.0447 3 0 22245 loss PDLIM4 PDZ and LIM domain 4 p10-83063481 0.0447 3 0 20835 loss p7-67493760 0.0447 3 0 28464 loss LOC100134279 hypothetical protein LOC100134279 p7-67493760 0.0447 3 0 28464 loss LOC100134576 hypothetical protein LOC100134576 p12-38866708 0.0448 3 0 18088 loss LRRK2 leucine-rich repeat kinase 2 p7-116178578 0.0448 3 0 11924 loss MET met proto-oncogene (hepatocyte growth factor receptor) p16-76346998 0.0449 3 0 16125 loss p9-10105598 0.0450 3 0 15984 gain or loss PTPRD protein tyrosine phosphatase, receptor type, D p1-44437498 0.0450 3 0 15544 gain DMAP1 DNA methyltransferase 1 associated protein 1 p15-31368537 0.0450 3 0 3532 gain or loss LOC440268 hypothetical LOC440268 p20-42436109 0.0450 3 0 4969 gain HNF4A hepatocyte nuclear factor 4, alpha p20-42442789 0.0450 3 0 15777 gain HNF4A hepatocyte nuclear factor 4, alpha p2-85944887 0.0450 3 0 33791 loss ST3GAL5 ST3 beta-galactoside alpha-2,3- sialyltransferase 5 p2-4201376 0.0451 0 7 183 neutral p5-6611291 0.0451 3 0 13916 gain or loss NSUN2 NOL1/NOP2/Sun domain family, member 2 p1-228113618 0.0451 3 0 41179 gain p12-16640890 0.0451 3 0 21832 loss LMO3 LIM domain only 3 (rhombotin-like 2) p16-73350258 0.0452 3 0 39223 loss FA2H fatty acid 2-hydroxylase p7-97302745 0.0452 3 0 4756 gain or loss ASNS asparagine synthetase p8-110432695 0.0452 3 0 2008 gain or loss p1-236099427 0.0452 3 0 2180 gain or loss ZP4 zona pellucida glycoprotein 4 p3-127194948 0.0452 3 0 8686 gain or loss SLC41A3 solute carrier family 41, member 3 p2-113763029 0.0453 3 0 13983 loss PAX8 paired box 8 p9-78316112 0.0453 3 0 16002 gain or loss p9-78340146 0.0453 3 0 13171 gain or loss LOC392352 Hypothetical LOC392352 p1-115054992 0.0453 3 0 12838 gain or loss CSDE1 cold shock domain containing E1, RNA- binding p1-115054992 0.0453 3 0 12838 gain or loss NRAS neuroblastoma RAS viral (v-ras) oncogene homolog p1-115054992 0.0453 3 0 12838 gain or loss NRAS neuroblastoma RAS viral (v-ras) oncogene homolog p1-115054992 0.0453 3 0 12838 gain or loss NRAS neuroblastoma RAS viral (v-ras) oncogene homolog p11-57907518 0.0453 3 0 17735 gain or loss p1-44552909 0.0454 3 0 2077 gain or loss PRNPIP prion protein interacting protein p6-38823656 0.0454 3 0 23245 loss DNAH8 dynein, axonemal, heavy chain 8 p11-18897434 0.0455 0 7 8203 neutral LOC390099 Similar to Sensory neuron-specific G-protein coupled receptor 2 p12-57570818 0.0455 3 0 44001 loss LRIG3 leucine-rich repeats and immunoglobulin-like domains 3 p16-13355154 0.0455 3 0 20053 loss p11-124179491 0.0456 3 0 12526 gain or loss p12-96506228 0.0456 3 0 3676 loss p9-29196194 0.0456 3 0 18756 loss LINGO2 leucine rich repeat and Ig domain containing 2 p18-8782044 0.0457 3 0 2256 loss KIAA0802 KIAA0802 p18-8782044 0.0457 3 0 2256 loss LOC284219 hypothetical protein LOC284219 p12-38799633 0.0458 3 0 62418 loss SLC2A13 solute carrier family 2 (facilitated glucose- transporter), member 13 p2-102318994 0.0459 3 0 36100 gain or loss IL18R1 interleukin 18 receptor 1 p2-102318994 0.0459 3 0 36100 gain or loss IL1RL1 interleukin 1 receptor-like 1 p2-4201031 0.0459 0 8 345 neutral p8-115719862 0.0460 0 8 102 neutral p1-164112969 0.0462 3 0 11669 gain or loss UCK2 uridine-cytidine kinase 2 p12-31893551 0.0464 0 7 10154 neutral LOC100132795 similar to hCG1812832 p14-22001467 0.0469 0 7 3032 neutral DAD1 defender against cell death 1 p14-22001467 0.0469 0 7 3032 neutral MGC40069 hypothetical protein MGC40069 p14-22001467 0.0469 0 7 3032 neutral OR6J1 olfactory receptor, family 6, subfamily J, member 1 p14-22001467 0.0469 0 7 3032 neutral TRA@ T cell receptor alpha locus p14-22001467 0.0469 0 7 3032 neutral TRAC T cell receptor alpha constant p14-22001467 0.0469 0 7 3032 neutral TRAJ17 T cell receptor alpha joining 17 p14-22001467 0.0469 0 7 3032 neutral TRAV20 T cell receptor alpha variable 20 p14-22001467 0.0469 0 7 3032 neutral TRAV8-3 T cell receptor alpha variable 8-3 p14-22001467 0.0469 0 7 3032 neutral TRD@ T cell receptor delta locus p14-22001467 0.0469 0 7 3032 neutral TRDV2 T cell receptor delta variable 2

In sum, we identity a total of 339 genomic regions that show distinct genomic copy number differences between different subgroups of benign and malignant samples. These genomic regions map to 740 known human genes and/or proteins, although the nature of several significant genomic regions is not yet known.

Example 2 Cross-Check of Significant Genomic Region Gene Lists Between all Four Analyses

The level of redundancy between the resulting gene lists generated by each comparison (Table 7) was examined with Venn diagrams. 2 genes out of 119 overlapped the FC vs. FA (Table 3), FVPTC vs. NHP (Table 4), and PTC vs. NHP (Table 5) lists of genes (FIG. 6A). We combined this list of 117 unique genes and compared it to the list of mapped genes from the Malignant vs. Benign analysis (Table 6). Our results indicate that 24 genes overlap all four gene lists (FIG. 6B). Further, another set of Venn comparisons demonstrates that the FC v. FA list contributes most of the genes (14/24) that overlap with the M vs. B gene list (FIG. 6C). In contrast, when combined, the FVPTC vs. NHP and PTC vs. NHP lists contributed only 10/24 of the genes shared with the M vs. B list (FIG. 6D).

Further, we examined mRNA gene expression and alternative exon use with a different cohort of thyroid samples. That analysis led to the discovery of 4918 genes that can distinguish malignant thyroid nodules from benign. See U.S. patent application Ser. No. 12/592,065, which is hereby incorporated by reference in its entirety. In the study described here we have focused discovery efforts on DNA copy number analysis and thus far have found 339 genomic regions mapping to at least 292 genes that can distinguish thyroid pathology. We compared the older list of “mRNA genes” with the new list of “DNA copy number genes” and find that 73 genes overlap both lists, strengthening the importance of these genes as markers of thyroid pathology (FIG. 7).

Table 7. Total number of significant genomic regions (PLINK Features) and genes generated by each comparison.

TABLE 7 Genomic Regions In depth (PLINK Genes Thyroid Subtype Comparison Description Features) Mapped FC vs. FA (n = 4 vs. 20) Table 3 47 76 FVPTC vs. NHP (n = 12 vs. 20) Table 4 30 32 PTC vs. NHP (n = 15 vs. 20) Table 5 12 11 Combined list: FIG. 6A 89 119 (FC vs. FA) + (FVPTC vs. NHP) + (PTC vs. NHP) Malignant vs. Benign (n = 31 vs. 55) Table 6 250 199

Example 3 An Exemplary Device for Molecular Profiling

In some preferred embodiments, the molecular profiling business of the present invention compiles the list of DNA sequences of Table 1, 3, 4, 5, 6 or 8, or lists 1-45, or a combination thereof that are correlated with a polymorphism such as a copy number variation between benign and malignant, benign and normal, or malignant and normal samples. A subset of the genes or DNA sequences are chosen for use in the diagnosis of biological samples by the molecular profiling business. Compositions of short (i.e. 12-25 nucleotide long) oligonucleotides complimentary to the subset of genomic regions chosen for use by the molecular profiling business are synthesized by standard methods known in the art and immobilized on a solid support such as nitrocellulose, glass, a polymer, or a chip at known positions on the solid support.

Example 4 Molecular Profiling of a Biological Sample

Ribonucleic acid is extracted and from the biological sample, labeled with a detectable fluorescent label and hybridized to the solid support bound oligonucleotides under stringent conditions. Unhybridized ribonucleic acid is washed away and the amount of bound ribonucleic acid is determined photometrically by measuring raw fluorescent intensity values at each known oligonucleotide position. The raw fluorescence intensity values are normalized and filtered and converted to gene expression product levels. The gene expression product levels are input to a pre-classifier algorithm which corrects the gene expression product levels for the cell-type composition of the biological sample. The corrected gene expression product levels are input to a trained algorithm for classifying the biological sample as benign, malignant, or normal. The trained algorithm provides a record of its output including a diagnosis, and a confidence level.

Example 5 Molecular Profiling of Thyroid Nodule

An individual notices a lump on his thyroid. The individual consults his family physician. The family physician decides to obtain a sample from the lump and subject it to molecular profiling analysis. Said physician uses a kit from the molecular profiling business to obtain the sample via fine needle aspiration, perform an adequacy test, store the sample in a liquid based cytology solution, and send it to the molecular profiling business. The molecular profiling business divides the sample for cytological analysis of one part and for the remainder of the sample extracts nucleic acid from the sample, analyzes the quality and suitability of the mRNA and DNA samples extracted, and analyses the expression levels, alternative exon usage, and genomic DNA marker copy number variation of a subset of the genes listed in Tables 1, 3, 4, 5, 6 or 8. In this case, the particular markers (DNA or RNA) profiled are determined by the sample type, by the preliminary diagnosis of the physician, and by the molecular profiling company.

The molecular profiling business analyses the data and provides a resulting diagnosis to the individual's physician as illustrated in FIG. 4. The results provide 1) a list of DNA markers profiled, 2) the results of the profiling (e.g. the DNA marker copy number normalized to a standard such as the DNA marker copy number from nucleic acid isolated from tissue obtained by buccal swab, 3) the DNA marker copy number expected for normal tissue of matching type, and 4) a diagnosis and recommended treatment based on the results of the molecular profiling. The molecular profiling business bills the individual's insurance provider for products and services rendered.

Example 6 Molecular Profiling is an Improvement over Cytological Examination Alone

An individual notices a suspicious lump on her thyroid. The individual consults her primary care physician who examines the individual and refers her to an endocrinologist. The endocrinologist obtains a sample via fine needle aspiration, and sends the sample to a cytological testing laboratory. The cytological testing laboratory performs routine cytological testing on a portion of the fine needle aspirate, the results of which are ambiguous (i.e. indeterminate). The cytological testing laboratory suggests to the endocrinologist that the remaining sample may be suitable for molecular profiling, and the endocrinologist agrees.

The remaining sample is analyzed using the methods and compositions herein. The results of the molecular profiling analysis suggest a high probability of early stage follicular cell carcinoma. The results further suggest that molecular profiling analysis combined with patient data including patient age, and lump or nodule size indicates thyroidectomy followed by radioactive iodine ablation. The endocrinologist reviews the results and prescribes the recommended therapy.

The cytological testing laboratory bills the endocrinologist for routine cytological tests and for the molecular profiling. The endocrinologist remits payment to the cytological testing laboratory and bills the individual's insurance provider for all products and services rendered. The cytological testing laboratory passes on payment for molecular profiling to the molecular profiling business and withholds a small differential.

Example 7 Molecular Profiling Performed by a Third Party

An individual complains to her physician about a suspicious lump on her neck. The physician examines the individual, and prescribes a molecular profiling test and a follow up examination pending the results. The individual visits a clinical testing laboratory also known as a CLIA lab. The CLIA lab is licensed to perform molecular profiling of the current invention. The individual provides a sample at the CLIA lab via fine needle aspiration, and the sample is analyzed using the molecular profiling methods and compositions herein. The results of the molecular profiling are electronically communicated to the individual's physician, and the individual is contacted to schedule a follow up examination. The physician presents the results of the molecular profiling to the individual and prescribes a therapy.

While preferred embodiments of the present invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in practicing the invention. It is intended that the following claims define the scope of the invention and that methods and structures within the scope of these claims and their equivalents be covered thereby.

INCORPORATION BY REFERENCE

All publications and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication or patent application was specifically and individually indicated to be incorporated by reference. 

1-30. (canceled)
 31. A method for diagnosing thyroid disease in a subject, the method comprising: (a) providing a biological sample from a subject; (b) assaying said biological sample for at least one gene expression level associated with one or more genes selected from Tables 1, 3-6, and 8, lists 1-45, and their complements; (c) identifying, in said biological sample, the presence of one or more polymorphisms of said one or more genes based on said at least one gene expression level of (b); and (d) determining whether said subject has or is likely to have a malignant or benign thyroid condition based on the result of (c).
 32. The method of claim 31, wherein the malignant thyroid condition is selected form the group consisting of follicular carcinoma, follicular variant of papillary carcinoma, and papillary thyroid carcinoma.
 33. The method of claim 31, wherein said benign thyroid condition is selected from the group consisting of follicular adenoma and nodular hyperplasia.
 34. The method of claim 31, wherein said biological sample comprises thyroid tissue.
 35. The method of claim 31, wherein said biological sample is a fine needle aspirate (FNA).
 36. The method of claim 31, wherein said one or more polymorphisms comprise a variation in copy number as compared to a normal sample.
 37. The method of claim 36, wherein said variation in copy number comprises a deletion.
 38. The method of claim 36, wherein said variation in copy number comprises an increase in copy number.
 39. The method of claim 36, wherein said normal sample comprises a biological sample from the same subject.
 40. The method of claim 36, wherein said normal sample comprises a biological sample from a different subject.
 41. The method of claim 31, wherein said biological sample is an RNA sample.
 42. The method of claim 31, wherein said one or more genes are selected from IYD, PSD4, Clorf68, KPRP, LCE1E, ST3GAL5, PKHD1L1, PDE8B and ZNF610.
 43. The method of claim 42, wherein said one or more genes are selected from ST3GAL5, PKHD1L1, PDE8B and ZNF610.
 44. The method of claim 31, wherein said one or more genes are associated with one or more ontology groups and/or signaling pathways.
 45. The method of claim 44, wherein said ontology groups comprise receptor tyrosine kinases, cytoplasmic tyrosine kinases, GTPases, serine/threonine kinases, lipid kinases, mitogens, growth factors, transcription factors or combination thereof.
 46. The method of claim 44, wherein said signaling pathways comprise epithelial growth factor signaling pathway and/or mitogen-activated protein kinases.
 47. The method of claim 31, further comprising providing a therapeutic intervention to the subject based on the result of (d).
 48. The method of claim 31, further assaying said biological sample for at least one gene expression level associated with two or more genes selected from Tables 1, 3-6, and 8, lists 1-45, and their complements.
 49. The method of claim 31, further comprising detecting two or more polymorphisms selected from PAX8, IYD, PSD4, Clorf68, KPRP, LCE1E, ST3GAL5, PKHD1L1, PDE8B and ZNF610.
 50. The method of claim 49, further comprising detecting two or more polymorphisms selected from ST3GAL5, PKHD1L1, PDE8B and ZNF610.
 51. The method of claim 31, further comprising performing cytological and/or histological testing on a sample from said subject.
 52. The method of claim 51, wherein said sample for cytological and/or histological testing is the same as said biological sample.
 53. The method of claim 51, wherein said cytological testing and/or histological testing is inconclusive as to whether said subject has a malignant or benign thyroid condition.
 54. The method of claim 31, wherein said identifying of (c) is achieved by applying a machine learning algorithm.
 55. The method of claim 31, wherein said determining of (d) has a sensitivity greater than 95%.
 56. The method of claim 31, wherein said determining of (d) has a negative predictive value (NPV) greater than 95%. 